Moonshot AI released Kimi K3, a 2.8-trillion-parameter Mixture-of-Experts model, through its API on 16 July 2026, with full weights following on Hugging Face on 26-27 July. Alibaba's Qwen team made Qwen3.8-Max, a 2.4-trillion-parameter MoE, generally available on 3 August 2026 and committed to publishing its weights the following week.

Both claim frontier-class performance. Both ship a 1-million-token context window. Both are natively multimodal. Both are priced at a fraction of the leading proprietary models. And both are now live on the Qubrid AI inference platform with day-0 access.

This post is the head-to-head. Every architectural claim, benchmark number and price below is attributed to a named primary source, and where two vendors report the same benchmark with different numbers, we say so instead of quietly picking the flattering one. If you are choosing between these two models for a production workload, this is the document to read.

TL;DR: the short version

1. Specification sheet, side by side

Sources: the Kimi K3 model card on Hugging Face for all K3 architecture figures; MarkTechPost's 3 August release analysis and DataCamp's 5 August write-up for Qwen3.8-Max specifications and API limits. Qubrid pricing is from the Qubrid AI model catalogue.

The asymmetry in that table is the first real finding, and it matters more than any single benchmark. Moonshot published a full architecture card. Alibaba published a parameter count. Everything in the Kimi K3 column can be checked against a file you can download; most of the Qwen3.8-Max column is a number in a launch post. Until the Qwen weights and model card land, that gap is a genuine procurement risk factor, not a stylistic difference.

2. Architecture: what is actually new

2.1 Kimi K3: Kimi Delta Attention, Attention Residuals and Stable LatentMoE

Kimi K3's model card describes three architectural pillars, and they are the reason a 2.8T model is servable at all.

Kimi Delta Attention (KDA). K3 is not a uniform-attention transformer. Of its 93 layers, 69 use KDA and 24 use Gated Multi-head Latent Attention. This hybrid layout is the core efficiency mechanism. As reported by Kili Technology in its August analysis, KDA delivers roughly 6.3x faster decoding relative to the previous generation. Moonshot shipped a vLLM implementation of KDA at the same time as the weights, which is why day-0 self-hosting was technically possible at all rather than being a three-week community port.

Attention Residuals (AttnRes). The second mechanism adds residual pathways across attention layers. Kili Technology reports the effect as approximately 25% better token efficiency for around 2% additional compute cost. If that ratio holds on your workload, it is the single most economically significant number in the K3 architecture, because token efficiency is what you actually pay for.

Stable LatentMoE. K3 routes 16 of 896 experts per token, plus 2 shared experts, giving 104B activated parameters out of 2.8T total. That is a sparsity ratio of roughly 1:27. Moonshot's own framing on the model card is that this yields approximately a 2.5x improvement in overall scaling efficiency compared with Kimi K2.

Native MXFP4. This is the underrated one. K3 applies quantization-aware training from the supervised fine-tuning stage onward, with MXFP4 weights and MXFP8 activations. The practical consequence: K3 is not a BF16 model that someone later squeezed into 4 bits and hoped for the best. The 4-bit form is the trained form. Any team that has watched a post-hoc GPTQ or AWQ quantization degrade tool-calling reliability will recognise why this matters.

Vision. MoonViT-V2 at 401M parameters is fused into the same model rather than bolted on, which is what "native multimodal" means here in practice.

2.2 Qwen3.8-Max: scale on the Qwen 3.5 foundation

Alibaba has been considerably less forthcoming. What is confirmed:

• 2.4 trillion total parameters, roughly 95 billion active per token, a sparsity ratio of about 1:25, which is close to K3's.

• Built on the architectural foundation of Qwen 3.5, as reported by DataCamp and Investing.com's coverage of the launch.

• Native text, image and video input.

• A 1M-token context window with a hard split: maximum input 991K tokens, dropping to 983K when thinking is enabled, maximum output 131K in both modes, and a maximum reasoning budget of 262K tokens, per MarkTechPost's reading of the QwenCloud model page.

• Published rate limits of 2M tokens per minute and 15K requests per minute.

What is not confirmed: layer count, expert count, routing scheme, attention mechanism, activation function, vision encoder, tokenizer, or native precision. MarkTechPost flagged this directly in its launch analysis, noting that with the activated-parameter count undisclosed at the time of writing, real serving cost for the open weight could not be modelled.

Where Qwen3.8-Max does publish detail is at the systems and behaviour layer rather than the tensor layer:

• Dynamic Workflows, a mechanism for programmatically planning a task and dispatching sub-agents in parallel. In Alibaba's quantitative-research demonstration, the model decomposed six factor descriptions into fifty research directions each, dispatched roughly 330 sub-agents, and ran around 6,000 backtests, pruning factors mid-run when overfitting signals appeared, as documented in DataCamp's write-up.

• Vision in the execution loop, not just at the input boundary. The model inspects its own intermediate visual output, identifies problems such as a misaligned interface, and revises its plan.

• Qwen-MM-Plugins, an extension library that adds image and video processing, visual tool use and multimodal memory to existing agent frameworks, as reported by The Decoder.

• RecreationBench, a new benchmark Alibaba introduced alongside the model that requires rebuilding a running application without source-code access, observing the target only through clicks and keyboard input, across Ubuntu, macOS, Windows, Android and web.

2.3 Reading the two design philosophies

Strip away the marketing and the two teams optimised for different things.

Moonshot spent its innovation budget on making a very large model cheap to decode: hybrid linear-ish attention, residual attention pathways, extreme expert sparsity, and 4-bit-native training. The result is a model that costs $15 per million output tokens but, per Artificial Analysis, uses 21% fewer output tokens than its predecessor to reach a 13-point Intelligence Index gain.

Alibaba spent its budget on making a model that survives long unattended runs: feedback-loop training, parallel sub-agent dispatch, vision-in-the-loop self-correction, and reinforcement learning across thousands of environments. The result is a model that costs $6 per million output tokens but, per Artificial Analysis, now takes 64 turns to complete a GDPval-AA task where its predecessor took 14.

Neither approach is wrong. They produce very different cost curves, which we quantify in section 6.

3. Benchmarks: how to compare two vendor tables honestly

Here is the methodological problem nobody addresses. Moonshot published a K3 benchmark table. Alibaba published a Qwen3.8-Max benchmark table. They do not share a row format, they do not always share benchmark versions, and they used different agent harnesses.

Moonshot's own footnotes are unusually explicit about this. The K3 model card states that K3 was evaluated on Terminal-Bench 2.1 with the Kimi Code harness while other models used their best available harness, that DeepSWE scores use the Kimi Code harness and K3 attains 67.3 rather than 67.5 with the mini-SWE-agent harness instead, and that on SWE-Marathon, Claude Fable 5 hit fallbacks on 35% of tasks in Moonshot's evaluation, which may have depressed its measured score.

So instead of comparing headline numbers directly, we apply a shared-anchor test: where both vendors report the same benchmark and both report the same third-party models with matching scores, the comparison between K3 and Qwen3.8-Max on that row is reliable. Where the anchors disagree, the row is not comparable and we flag it.

3.1 Rows that pass the shared-anchor test

These are the comparisons you can actually trust.

Kimi K3 scores from the Hugging Face model card. Qwen3.8-Max scores from the benchmark table reproduced by DataCamp from Alibaba's 3 August release. Terminal-Bench carries a partial-anchor caveat: the two tables agree on Opus 4.8 and GPT-5.6 Sol but disagree on Fable 5, at 88.0 versus 84.6.

The pattern that emerges is clean and, importantly, it is not a single-winner story:

• Kimi K3 leads on terminal-driven engineering and hard science reasoning.

• Qwen3.8-Max leads on desktop agentic control and visual perception.

3.2 Rows that fail the shared-anchor test

FrontierSWE. K3 reports 81.2, Qwen3.8-Max reports 73.5. But Moonshot's table puts Claude Fable 5 at 86.6 while Alibaba's puts it at 88.8, and Opus 4.8 at 66.7 versus 70.0. The gap direction favours K3 either way, but the magnitude is not trustworthy.

Agents' Last Exam. K3 reports 28.3 with GPT-5.6 Sol at 29.6. Qwen3.8-Max reports 52.4 with GPT-5.6 Sol at 53.6. These are not the same measurement. Moonshot's footnote specifies the official leaderboard's primary pass-rate metric as of 23 July 2026. Anyone quoting "Qwen 52.4 beats Kimi 28.3" is comparing two different scales.

DeepSWE. K3 reports 67.5 on DeepSWE v1.1 tasks with the Kimi Code harness. Qwen reports 56.6 on "DeepSWE 1.1" as a generational improvement over Qwen3.7-Max's 21.6. Different harnesses, same nominal benchmark version.

OmniDocBench. K3 reports 91.1. Qwen reports 92.1 on OmniDocBench 1.5. Different versions.

SWE-bench Pro. Only Alibaba reports it, at 67.7 against Fable 5's 80.0. DataCamp called this Qwen's honest weak spot. Moonshot does not report SWE-bench Pro at all, so there is no comparison to make.

3.3 Independent evaluation: Artificial Analysis

This is where the vendor-table problem disappears, because one organisation ran both models through the same nine-evaluation pipeline.

Artificial Analysis Intelligence Index v4.1.1 comprises GDPval-AA v2, tau-cubed Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience and AA-LCR.

Kimi K3 figures are from Artificial Analysis's own K3 analysis article and its K3 model page. Qwen3.8-Max figures are from The Decoder's 6 August report citing the Artificial Analysis Qwen3.8-Max page directly. Artificial Analysis initially published a score of 53 for Qwen3.8-Max, then re-ran the evaluation on Alibaba's public API after intermittent endpoint issues, arriving at 56, as reported by OfficeChai.

Three things worth pulling out.

First, the aggregate gap is one point. Anyone telling you one of these models is decisively smarter than the other is over-reading a difference well inside the noise floor of a nine-benchmark composite.

Second, Qwen3.8-Max wins GDPval-AA and it is not close. 1,739 versus 1,685 Elo. On Artificial Analysis's own numbers, only Claude Opus 5 at 1,852 scores higher. That is a real result on a benchmark spanning real-world occupational tasks.

Third, the mechanism behind that win is expensive. The Decoder reports that Qwen3.8-Max needs 64 steps per task instead of Qwen3.7-Max's 14, and that input token consumption grew roughly 15x because the evaluation resends full conversation history at each step. A single Intelligence Index task now costs $1.14 against Qwen3.7-Max's $0.53, more than double, despite Alibaba having cut per-token prices. Kimi K3 scores one point higher at $0.86 per task.

That is the sentence to take into a budget meeting: on independent testing, Kimi K3 currently delivers marginally higher aggregate intelligence at roughly 25% lower cost per completed task, even though its per-token price is 2.5x higher on output.

3.4 The hallucination trade

Both models made the same trade, in different directions and to different degrees.

Artificial Analysis reports that K3's AA-Omniscience accuracy rose from 33% to 46% over Kimi K2.6, moving the composite AA-Omniscience Index from +6 to +18, while its hallucination rate rose from 39% to 51%. As Kili Technology framed it in its analysis, the same release that made K3 the leading open-weights model on the Intelligence Index also made it meaningfully more likely to answer confidently when it does not know. For calibration, Kili notes Claude Fable 5 hallucinates at 54.9% on the same measure.

Qwen3.8-Max moved the other way on the index and in the same direction on the rate. The Decoder reports AA-Omniscience fell 10 points versus Qwen3.7-Max, with accuracy holding around 31% while the hallucination rate climbed from 23% to 40%.

Practical read for engineering teams: neither model should be trusted as an unverified knowledge oracle. Both are substantially safer inside a loop with a verifier, whether that is a test suite, a retrieval grounding step, a rubric, or a human approval gate. This is not a knock on either release. It is the actual operating envelope both vendors' own evaluation partners are describing.

4. Long-horizon and agentic behaviour

Both labs built their launch narrative around multi-day autonomy, and both published showcase runs. These are vendor-run demonstrations, not independent reproductions, and should be read as capability existence proofs rather than benchmarks.

Kimi K3's showcases, as documented by Trilogy AI's launch analysis:

• A 24-hour GPU kernel arena in which K3 rewrote and tested kernels across NVIDIA H200 and alternative-vendor hardware. On the Attention Residuals task, the published trace ended at a 59.7% speedup over baseline against 57.1% for Claude Fable 5, with the caveat that Fable 5 was evaluated by a third party and may include fallback behaviour.

• MiniTriton, a compact compiler with a tile-level intermediate representation over MLIR, optimisation passes and PTX generation, with Moonshot reporting performance matching or beating Triton on some supported roofline workloads plus stable nanoGPT training through the resulting stack.

• A 48-hour agent run that designed and verified a 4 mm² chip with 1.46 million standard cells, 0.277 MB of SRAM, an INT4 MAC array and timing closure at 100 MHz.

Qwen3.8-Max's showcases, as documented by DataCamp and OfficeChai:

• A roughly 16-day fully autonomous coding run that built a self-evolving CLI harness accumulating 265 commits, 127 pull requests and 151 issues, with requirements entering as GitHub issues and agents claiming, executing, testing and merging them.

• A roughly 125-hour research reproduction in which the model wrote about 7,600 lines of code, took over 1,100 actions, ran 33 rounds of GPU training with no starter code, reproduced a paper's six main findings, then ran a self-improving loop across 4 rounds and 18 ideas and landed on a method beating the paper's own approach by 2.71 points on AIME24.

• Entry into a live Tianchi competition against 526 human teams.

• The Dynamic Workflows quant-research run described in section 2.2, where selected factors reached excess Sharpe ratios of 0.64 to 1.48.

How to read these. DataCamp's own caveat is the correct one: a controlled multi-day autonomous run is not the same as pointing a model at your production monorepo and walking away. What the showcases do establish is that both models can hold a coherent objective across hundreds of tool calls without collapsing, which two generations ago was the binding constraint. Qwen's demonstrations skew toward duration and self-direction. Kimi's skew toward depth in systems-level engineering, kernels, compilers and silicon.

5. Openness: weights, licences and what "open source" actually means here

This is where the two releases diverge most sharply, and it is the section most comparisons skip.

5.1 Kimi K3: weights shipped, licence is bespoke

Moonshot published the full K3 weights on Hugging Face on 26 July 2026, a day ahead of its stated 27 July target. Simon Willison, who tracked the release, noted the repository weighs 1.56 TB.

The licence is the part that requires actual reading. Widespread early coverage described it as "Modified MIT," carrying forward the label from Kimi K2. That is wrong for K3. As Willison observed, the K3 licence no longer describes itself as modified MIT and goes further than K2's. Hugging Face lists it under the custom identifier kimi-k3 rather than any standard SPDX identifier.

What the Kimi K3 License actually does, per the licence-focused analyses published by Kingy AI, Future Stack Reviews and Towards AI, and per the LICENSE file itself:

• Charges no fee, and permits commercial use, modification, distribution and sale.

• Requires a separate agreement with Moonshot for a defined class of Model-as-a-Service operator once the licensee and its affiliates exceed an aggregate revenue threshold of $20 million in any trailing 12-month period. Critically, that threshold counts total group revenue, not revenue earned from K3.

• Requires products above 100 million monthly active users or $20 million monthly revenue to prominently display "Kimi K3."

• Exempts internal use, defined as use that does not make the software, its outputs or its underlying capabilities available to third parties, and use through Moonshot's official products or certified inference partners.

Kingy AI's assessment is the accurate label: open-weight under a custom, broadly permissive licence, not unconditionally open source. The Open Source Initiative's Open Source AI Definition requires usability for any purpose without asking permission, and K3's separate-agreement clause means that permission is not universal.

Who this actually affects: almost nobody building an application. If you are embedding K3 as a feature inside a product, the MaaS clause generally does not apply. If you are standing up a public multi-tenant inference API on K3 weights and your group revenue clears $20M, you need Moonshot's paperwork. That is a narrow class, but it is exactly the class that publishes "open source model" headlines.

5.2 Qwen3.8-Max: announced, not yet shipped

Alibaba's commitment is significant in principle: this would be the first Max-class Qwen model released with open weights, breaking a pattern where Qwen3.7-Max and every prior Max-tier release stayed API-only while the open line continued separately with Qwen3.6.

But the tense matters, and several outlets have been careful about it. Techsy's 4 August audit checked Hugging Face directly and found no Alibaba model card for Qwen3.8, only community uploads named "Qwen3.8_4B_Distilled" and variants that are third-party distils rather than the flagship. As of 5 August, neither Qwen3.8-Max nor Qwen3.8-27B had a model card on Hugging Face and no licence had been named. AI/TLDR dates the commitment specifically to the week of 10 August 2026.

The honest state of play at the time of writing: Kimi K3 is an open-weight model with a published, readable, slightly restrictive licence. Qwen3.8-Max is a hosted model with an open-weight commitment and an unpublished licence. Those are different procurement objects. If open weights are a hard requirement for your deployment, K3 is the only one of the two you can act on today, and Techsy's point stands that coverage collapsing "available" into "open" is doing real damage to buyer clarity.

One further practical note for anyone planning around the Qwen weights: MarkTechPost's assessment is that Qwen3.8-27B, not the flagship, is the realistic on-premise deployment path for most organisations. That is almost certainly correct, and it echoes the r/LocalLLaMA discussion DataCamp summarised, where several commenters were more interested in squeezing intelligence onto a single consumer GPU than in a 2.4T model only a handful of setups can run.

6. Self-hosting reality check

Both of these are data-centre models that happen to be open. Here is what that costs.

6.1 Kimi K3 hardware

The numbers reported across independent guides are worth reconciling, because they look contradictory and are not.

• Repository size: 1.56 TB. This is the full Hugging Face repo, which includes multiple tensor types.

• MXFP4 weight size: approximately 594 GB. This is the trained 4-bit form you actually serve.

• vLLM minimum VRAM estimate: approximately 1,680 GB. Kingy AI reports the official vLLM metadata figure. Weights are the floor, not the requirement. KV cache at 1M context and concurrent request state dominate the rest.

• Minimum viable configuration: the official vLLM recipe starts at 8x NVIDIA GB300 or 8x AMD MI355X/MI350X. GEO Toolbox reports the vLLM team's floor as one 8x B300 node or a GB300 NVL72, with a minimum of 16x B200 or GB200 on the prior generation, and notes AMD ROCm support from launch. Guides working from the raw 594 GB figure cite 8x H100 80GB as the bare load floor, which fits the weights but leaves nothing for serving.

• Moonshot's own production recommendation: a high-bandwidth supernode of 64 or more accelerators, as reported by Kingy AI.

Supported inference engines are vLLM, SGLang and TokenSpeed, with Moonshot publishing recipes for each.

The framing that matters operationally is this: the hardware maths is arithmetic, but keeping a 16-to-64 GPU deployment healthy, utilised and cost-sane is an ongoing engineering commitment, and that is where teams actually get stuck rather than at provisioning.

6.2 Qwen3.8-Max hardware

Not yet modellable. With no released checkpoint, no published precision and no model card, serving cost cannot be estimated with any rigour. At 2.4T total parameters with ~95B active, the shape will resemble K3's: aggregate GPU memory in the terabytes, expert-parallel and tensor-parallel traffic saturating the interconnect, multi-node orchestration mandatory.

6.3 The break-even question

The activated-parameter figure is the one that gets misread most often, and the correction follows directly from Moonshot's own architecture table: 104B activated describes the model capacity used per token step, not what you need to store. The router selects a different 16 of 896 experts at each step, so the complete expert set stays resident in the deployed model. Sizing a deployment against 104B rather than 2.8T is the single most expensive mistake available here.

Which produces the practical conclusion for the overwhelming majority of teams: for anything short of sustained, high-volume, latency-critical production traffic, a hosted endpoint beats self-hosting on total cost of ownership by a wide margin. Self-hosting these models makes financial sense at the point where your token spend exceeds the amortised cost of a dedicated multi-node cluster plus the engineers to run it. That crossover point is real and reachable, but it is much further out than launch-week enthusiasm suggests. This is precisely why Qubrid runs both a serverless per-token tier and a dedicated GPU tier: the correct answer changes as your volume grows, and it should be possible to change your mind without changing vendors.

7. Pricing: the full cost model

7.1 Headline rates

Qubrid list prices are $3.00 / $15 / $0.30 for Kimi K3 and $2.3 / $5.69 / $0.16 for Qwen3.8-Max.

Kimi puts it at the same level as Anthropic's Sonnet tier and makes it, as Simon Willison noted at launch, a significant increase over Kimi K2.6's $0.95/$4. That was a deliberate positioning move. The pitch is no longer "cheap"; it is "frontier capability, openly licensed."

7.2 Blended cost, which is what you actually pay

Headline rates mislead because real workloads are not 1:1 input to output. Here are three realistic profiles, priced on Qubrid rates.

Profile A: RAG and document QA. Long inputs, short answers. 50,000 input tokens, 1,000 output tokens per request, no cache hit.

• Kimi K3: (0.050 x $3.00) + (0.001 x $15.00) = $0.165 per request

• Qwen3.8-Max: (0.050 x $2.30) + (0.001 x $5.69) = $0.121 per request

• Qwen is roughly 27% cheaper.

Profile B: Coding agent, cache-warm. Repository context held in cache. 200,000 cached input tokens, 5,000 fresh input tokens, 8,000 output tokens.

• Kimi K3: (0.200 x $0.30) + (0.005 x $3.00) + (0.008 x $15.00) = $0.195 per request

• Qwen3.8-Max: (0.200 x $0.16) + (0.005 x $2.30) + (0.008 x $5.69) = $0.089 per request

• Qwen is roughly 54% cheaper per request.

Profile C: Long-horizon agentic task. This is where the arithmetic inverts. Independent Artificial Analysis measurement, not modelling: $0.86 per Intelligence Index task for Kimi K3 versus $1.14 for Qwen3.8-Max. Qwen's lower per-token price is more than consumed by taking 64 turns where K3 takes fewer, and by resending conversation history at each step.

The rule that falls out of this:

On bounded, single-shot or cache-warm requests, Qwen3.8-Max is meaningfully cheaper. On open-ended multi-turn agentic tasks, Kimi K3 is cheaper in practice despite costing 2.5x more per output token, because it finishes in fewer turns and fewer tokens.

Do not choose on the rate card. Choose on measured cost per completed task, on your own workload.

7.3 Cache economics

For Qwen3.8-Max, MarkTechPost's analysis makes the sharpest observation on the QwenCloud rate card: cached input is eight times cheaper than fresh input, which means prefix stability drives cost more than prompt length does. On Qubrid the ratio is steeper still, at 14.4x ($2.30 fresh versus $0.16 cached). If you are building an agent with a large stable system prompt, tool schema and repository context, structuring that prefix to be byte-identical across calls is the single highest-leverage cost optimisation available to you. It will outperform any amount of prompt trimming.

Kimi K3's cache ratio is 10x ($3.00 versus $0.30). The same principle applies with slightly less leverage.

8. What the community says

Independent, non-vendor signal is thin this early, but it exists and it is worth reporting accurately rather than inventing consensus.

On the head-to-head. Trilogy AI ran a matched StackPerf evaluation in which both models inspected 269 files across two unfamiliar frozen repositories and had to produce an integration design with exact repository citations for every claim, plus a data contract, migration plan, tests, risks and evidence ledger. After blind review and factual penalties, Kimi scored 83 out of 100 and Qwen scored 80. Their qualitative read is more useful than the three-point gap: Qwen defined a cleaner boundary between the two systems and captured stronger replay metadata, while Kimi handled revisions, regeneration and scene history more completely.

On raw generation quality. A widely shared X post from developer Loktar ran identical shader-generation prompts through both models, reporting that both produced correct output zero-shot with no mistakes, and that Qwen consumed far more tokens to get there. One reply captured the trade-off with unusual precision, characterising Qwen's extra token consumption as showing its work rather than thinking, because it does not trust itself. Whether or not you accept that framing, the token-consumption observation matches Artificial Analysis's independent turn-count and token-growth data exactly, which is a genuinely useful convergence of anecdote and measurement.

On leaderboards and arenas. Kimi K3 holds the top position on Arena.ai's Frontend Code arena, surpassing Claude Fable 5, as reported by both Simon Willison and Trilogy AI, and ranks #1 for Code - WebDev on the same platform.

On the r/LocalLLaMA reaction. DataCamp's summary of the Qwen3.8-Max discussion found the more interesting debate was not about the flagship at all, but about the smaller Qwen3.8-27B, with several commenters more excited about a model that fits a single consumer GPU than a 2.4T model only a handful of setups can run. During the July preview, MarkTechPost characterised the same subreddit's response as dominated by 2.4T serving maths and hope for a smaller or distilled variant. DataCamp also relays a commenter observation that Qwen models tend to overthink, and that DeepSeek V4 Flash spends less time reasoning even when output quality is lower, with the practical advice to drop reasoning_effort to low before assuming the default is right for latency-sensitive work.

On the licence. The strongest community pushback on K3 has been about labelling rather than capability. Multiple independent analyses converged on the same finding within a week: the "Modified MIT" label carried over from K2 is incorrect for K3, and teams should read the LICENSE file in the repository rather than rely on a 2025 memory of K2's terms.

On the numbers nobody charted. Digital Applied's analysis makes the point that K3's Intelligence Index result and its hallucination-rate regression are both true, and only one of them made the launch coverage. That is a fair criticism of the ecosystem, including of benchmark-led marketing generally, and it is why the hallucination figures appear in section 3.4 of this post rather than in a footnote.

9. Run both models yourself on Qubrid AI

Everything above is a reason to test, not a substitute for testing. Both models are live on Qubrid right now with day-0 access and an OpenAI-compatible endpoint, which means switching between them is a one-line change. If you already have the openai Python package installed, you are about ninety seconds from a side-by-side comparison on your own prompts.

Base URL: https://platform.qubrid.com/v1

Grab an API key at platform.qubrid.com, then run both snippets below back to back.

9.1 Kimi K3, with vision and maximum reasoning effort

from openai import OpenAI

# Initialize the OpenAI client with Qubrid base URL
client = OpenAI(
    base_url="https://platform.qubrid.com/v1",
    api_key="QUBRID_API_KEY",
)

response = client.chat.completions.create(
    # Must match the exact model ID from the docs - variations will cause errors.
    model="moonshotai/Kimi-K3",
    messages=[
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "What is in this image? Describe the main elements."
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
            }
          }
        ]
      }
    ],
    max_tokens=300,
    temperature=0.7,
    stream=False,
    extra_body={
        "reasoning_effort": "max",
    }
)

print(response.choices[0].message.content)

Three things to know before you wire this into anything real.

reasoning_effort goes inside extra_body, not at the top level of the request. It accepts "low", "high" and "max", and K3 defaults to "max". Drop it to "low" on latency-sensitive paths and measure the quality delta on your own tasks rather than assuming the default is correct.

The multimodal content array above is the native path, not a workaround. MoonViT-V2 is fused into the model, so image reasoning and text reasoning share the same forward pass. Swap the Britannica URL for one of your own charts, screenshots or scanned documents and you are testing the capability that produced K3's 91.1 on OmniDocBench.

The integration detail that catches most teams: K3 always thinks and returns reasoning_content alongside content. Moonshot's model card is explicit that the model was trained in preserved-thinking-history mode, so on multi-turn conversations and tool calls you must append the complete assistant message back into messages as-is, including reasoning_content and tool_calls, not just content. Stripping reasoning content between turns is a habit carried over from other reasoning models and it will quietly degrade K3's multi-turn and tool-calling behaviour.

9.2 Qwen3.8-Max, with thinking enabled

from openai import OpenAI

# Initialize the OpenAI client with Qubrid base URL
client = OpenAI(
    base_url="https://platform.qubrid.com/v1",
    api_key="QUBRID_API_KEY",
)

response = client.chat.completions.create(
    # Must match the exact model ID from the docs - variations will cause errors.
    model="Qwen/Qwen3.8-Max",
    messages=[
      {
        "role": "user",
        "content": "Explain the main benefits of using a chat completion API for text generation."
      }
    ],
    max_tokens=4096,
    temperature=0.7,
    top_p=1,
    stream=False,
    extra_body={
        "enable_thinking": True,
    }
)

print(response.choices[0].message.content)

Note the difference in the thinking control. On Qubrid, Qwen3.8-Max exposes a boolean enable_thinking flag rather than the graded reasoning_effort levels documented on QwenCloud's first-party API. Set it to False for latency-sensitive or high-volume paths where you do not need extended reasoning, and remember that thinking tokens are billed as output tokens. Given Qwen3.8-Max's measured behaviour on long-horizon tasks, where Artificial Analysis recorded 64 turns per GDPval-AA task, this flag is the most consequential cost lever in the request body.

9.3 The comparison worth actually running

Reading benchmark tables is a poor substitute for ten minutes of your own evaluation. The two snippets above differ only in the model string and the extra_body key, so a realistic test is straightforward to build:

1. Take twenty real prompts from your own workload. Not benchmark prompts. Your actual tickets, documents, repo questions or agent tasks.

2. Run both models, logging tokens and wall-clock time per request, not just the output text. Cost per completed task is the metric that matters, and section 7.2 shows how sharply it diverges from the rate card.

3. Score the outputs blind. Trilogy AI's head-to-head found a three-point gap after blind review, which is close enough that unblinded judgement will not resolve it reliably.

4. Test the cache path separately. With cached input priced 14.4x below fresh input for Qwen3.8-Max and 10x below for Kimi K3 on Qubrid, prefix stability will move your bill more than model choice does.

Both models support function calling, structured outputs and long context on the same endpoint, so tool-using agent tests need no additional plumbing. Qubrid's serverless tier bills per token at the rates in section 7; if your sustained volume crosses the break-even point discussed in section 6.3, the same model can move onto dedicated GPUs without an application-code change.

10. Which model should you actually use

The honest summary. These models are one point apart on the best independent aggregate available. Choosing between them on leaderboard position is close to a coin flip. Choosing between them on workload shape is not: K3 for engineering depth and token efficiency, Qwen3.8-Max for desktop agency, visual work and long unattended autonomy. Most serious teams should run both against their own eval suite for a week. On Qubrid that costs a model-string change and a few dollars of tokens, which is a materially cheaper way to find out than reading anyone's benchmark table, including this one.

Frequently asked questions

Is Kimi K3 open source? Kimi K3 is open-weight, not open source in the OSI sense. The full weights are on Hugging Face under a custom "Kimi K3 License" that permits commercial use, modification and distribution at no fee, but requires a separate agreement for large Model-as-a-Service operators above $20M aggregate trailing-12-month group revenue, and requires products above 100M monthly users or $20M monthly revenue to display "Kimi K3." It is not the Modified MIT licence used for Kimi K2.

Is Qwen3.8-Max open source? Not at the time of writing. Alibaba announced that weights for Qwen3.8-Max and Qwen3.8-27B would be published on Hugging Face and ModelScope in the week of 10 August 2026, which would make it the first Max-class Qwen model released with open weights. No licence has been published. Check the repository and licence file directly before planning around it.

Which is bigger, Kimi K3 or Qwen3.8-Max? Kimi K3 at 2.8T total parameters versus Qwen3.8-Max at 2.4T. Activated parameters per token are 104B for K3 and approximately 95B for Qwen3.8-Max. Total size determines storage; activated size determines per-token compute.

Which is cheaper? Per token, Qwen3.8-Max, at $2.30/$5.69 on Qubrid versus $3.00/$15.00 for Kimi K3. Per completed agentic task, Kimi K3, at $0.86 versus $1.14 on Artificial Analysis's Intelligence Index measurement. Which figure matters depends entirely on whether your workload is bounded or open-ended.

Which has the longer context window? Effectively identical. Kimi K3 supports 1,048,576 tokens. Qwen3.8-Max supports 1M tokens with a maximum input of 991K, falling to 983K when thinking is enabled, and a maximum output of 131K.

Can I run either model on a single GPU? No. Kimi K3's MXFP4 weights are roughly 594 GB with an official vLLM VRAM estimate near 1,680 GB, a minimum recipe of 8x GB300 or 8x MI355X, and a production recommendation of 64 or more accelerators. Qwen3.8-Max's serving profile is not yet publishable. The smaller Qwen3.8-27B is the realistic single-node path once released.

Do both models support vision? Yes. Kimi K3 uses a fused MoonViT-V2 encoder at 401M parameters for native image understanding. Qwen3.8-Max accepts text, image and video natively and documents handling of 200-page-plus PDFs and videos over 100 hours.

Why does Kimi K3 cost so much more per output token than previous Kimi models? K3's $3/$15 is a substantial increase over Kimi K2.6's $0.95/$4. As Simon Willison observed at launch, this positions K3 alongside Anthropic's Sonnet tier. The pricing signals a deliberate shift from competing on cost to competing on capability, offset in practice by K3 using 21% fewer output tokens than K2.6 on Artificial Analysis's Intelligence Index.

Do these benchmark numbers come from the vendors or from independent testing? Both, and this post labels which is which. Vendor tables come from Moonshot's Hugging Face model card and Alibaba's 3 August release post as reproduced by DataCamp. Independent figures come from Artificial Analysis, Arena.ai, Vals AI and the public leaderboards Moonshot cites in its footnotes. Section 3 explains the shared-anchor method used to determine which cross-vendor comparisons are valid.

Sources

Every claim in this post is traceable to one of the following. Primary sources first.

Primary: vendor documentation

• Moonshot AI, Kimi K3 model card (Hugging Face) - full architecture table, evaluation results with harness footnotes, MXFP4 quantization detail, deployment guidance, preserved-thinking-history requirement.

• Kimi K3 License file - the actual licence text, not the "Modified MIT" label carried over from K2.

• Moonshot AI, Kimi K3 technical blog

• Kimi K3 technical report (PDF, GitHub)

• Kimi K3 vLLM deployment recipe and SGLang cookbook

• Qwen, Qwen3.8-Max release post - 3 August 2026 launch article and benchmark table.

• QwenCloud Qwen3.8-Max model page - context and token limits, rate limits, cache pricing, supported capabilities.

Independent evaluation

• Artificial Analysis, Kimi K3 model page

• Artificial Analysis, Qwen3.8-Max model page

• Artificial Analysis, "Kimi K3 achieves #3 in the Intelligence Index" - AA-Omniscience, AA-Briefcase, GDPval-AA v2, token efficiency.

• Trilogy AI, matched StackPerf head-to-head evaluation - the 269-file blind-reviewed comparison, Kimi 83 vs Qwen 80.

Analysis and technical reporting

• The Decoder, "Qwen3.8 Max catches Claude Opus 4.8 but Kimi K3 still scores higher for 25 percent less" (6 Aug 2026) - Intelligence Index re-run, turn counts, cost per task, AA-Omniscience regression.

• The Decoder, Qwen3.8-Max launch coverage - RecreationBench, Qwen-MM-Plugins, RL scaling.

• MarkTechPost, Qwen3.8-Max release analysis (3 Aug 2026) - context and token limits, cache economics, benchmark reading, deployment assessment.

• MarkTechPost, Qwen3.8-Max preview analysis (19 Jul 2026) - community sentiment method note.

• DataCamp, "Qwen3.8-Max: Features, Benchmarks, and Pricing" (5 Aug 2026) - full benchmark table, autonomous run details, Dynamic Workflows, r/LocalLLaMA summary.

• Simon Willison, "Kimi K3, and what we can still learn from the pelican benchmark" (16 Jul 2026) - pricing analysis, Arena.ai position, token efficiency.

• Simon Willison, "moonshotai/Kimi-K3" (27 Jul 2026) - weights release, 1.56 TB repo size, licence observation.

• Trilogy AI, "Kimi K3 Is Live: Pricing, Benchmarks, and the Wait for Public Weights" - kernel arena, MiniTriton, chip design run.

• Kili Technology, "Kimi K3's Benchmarks and Hallucinations" - KDA and AttnRes efficiency figures, hallucination analysis, Fable 5 calibration.

• Digital Applied, "Kimi K3 Benchmarks: The Hallucination Number Nobody Charted"

• OfficeChai, "Qwen 3.8 Max scores 56 on Artificial Analysis Intelligence Index" - score revision from 53 to 56, per-benchmark movement.

• OfficeChai, Qwen3.8-Max launch analysis - 10-day autonomous run, Tianchi competition entry.

Licensing

• Kingy AI, "Kimi K3 License: Commercial Use Is Allowed, with Conditions"

• Future Stack Reviews, "Kimi K3 License and API Terms"

• Towards AI, "Is Kimi K3 free to use commercially? The license, decoded"

Self-hosting and hardware

• Kingy AI, "Run Kimi K3 Locally? Hardware, VRAM and Cost Math" - 1,680 GB vLLM VRAM estimate, Moonshot's 64-accelerator supernode guidance.

• GEO Toolbox, "How to Run Kimi K3 Locally" - vLLM team floor configurations, AMD ROCm support.

• explainx.ai, "Run Kimi K3 Locally: Confirmed Hardware + vLLM" - confirmed download size and serve commands.

Open-weight status verification

• Techsy, "Qwen3.8: 2.4T Parameters, Open Weights, No Benchmarks" - direct Hugging Face audit, 4 August 2026.

• AI/TLDR, Qwen3.8-Max model brief - weight release date, explicit cache pricing.

Launch reporting and specification confirmation

• Neowin, Qwen3.8-Max release coverage

• Investing.com, Qwen3.8-Max announcement - Qwen 3.5 architectural foundation, 95B active parameters.

Community

• Loktar (@loktar00) on X, shader comparison thread

• Latent Space AINews, Qwen 3.8 Max coverage

All sources accessed 8 August 2026. Benchmark figures and open-weight status for Qwen3.8-Max were changing weekly at the time of writing; verify against the primary sources above before making a production decision.

Try both on Qubrid AI

Kimi K3 and Qwen3.8-Max are both live on Qubrid AI with day-0 access, a single OpenAI-compatible endpoint, and a shared API key. Kimi K3 runs at $3.00 / $15.00 / $0.30 per million input, output and cached input tokens. Qwen3.8-Max runs at $2.30 / $5.69 / $0.16, currently 20% below list.

The comparison in this post is as fair as public data allows. It still cannot tell you which model is better at your work. That answer costs a model-string change and a few dollars of tokens.

Get an API key and run the comparison in section 9