Qwen3.8 Max
Alibaba · Open Weight · rank 10 · bench-align-v5
Canonical idqwen3-8-max
Access typeOpen Weight
Blended $/1MUnavailable75% input / 25% output
Capability shape
Seven axes from the ranking source. A missing axis is drawn as a gap.
Runtime service evidence
Measured values with the date they were observed. Nothing is inferred from a sibling model or a provider claim.
Time to first token: latency to the first answer chunk (Artificial Analysis, via BenchLM). Reasoning models include thinking time, so values can run to tens or hundreds of seconds.
Evidence key: Observed
| Measurement | Value | Observed | Last good | Evidence |
|---|---|---|---|---|
| Time to first token | 51.78 s | 2026-09-17 | 2026-09-17 | |
| Throughput | 41 tok/s | 2026-09-17 | 2026-09-17 |
Regional or per-endpoint measurements appear only when the API supplies them; none are modelled here.
Endpoint and price matrix
Every published price component, including cache reads and writes.
Self-hosted listingQwen publishes the Qwen3.8-2.4T-A95B checkpoint for self-hosting under the custom Qwen3.8-Max License. Alibaba Cloud Model Studio's official pricing page separately lists the hosted qwen3.8-max SKU in both non-thinking and thinking modes for the 0 < Token <= 1M tier. The China and global tables list CNY 12 input / CNY 36 output per million tokens; the US international table lists CNY 14.988 input / CNY 44.965 output. The USD numeric fields stay null because we do not convert a non-USD first-party price.No hosted token rate was published for this model, so its per-token price is unavailable rather than zero.
Workload-aware monthly cost example
10 conversations per day × 8 messages × 22 active days, 1200 input and 400 output tokens per message, no cache. This uses the same calculator as the cost simulator, so an unavailable applicable rate makes the example unavailable too.
Modelled monthly costUnavailableThe applicable input rate is unavailable.
Modelled tokensUnavailable
Open the simulatorChange this workload
Benchmark record
60 matched benchmark rows with their published value, unit, and provenance.
Knowledge
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| GPQA Graduate-Level Google-Proof Q&A | 92.6 | 448 questions | Graduate level | GPQA: A Graduate-Level Google-Proof Q&A Benchmark |
| GPQA-D GPQA Diamond | 92.6 | Graduate-level science questions | Graduate level | Trinity-Large-Thinking: Scaling an Open Source Frontier Agent |
| HLE Humanity's Last Exam | 43.6 | Expert-level questions | Frontier expert level | Humanity's Last Exam |
| HLE w/o tools Humanity's Last Exam without tools | 43.6 | Expert-level questions | Frontier expert level | Introducing GPT-5.4 mini and nano |
| GPQA Diamond (Vals) GPQA Diamond, Vals AI run | 93.7 | Graduate-level science questions | Expert reasoning | Vals AI GPQA Diamond, Vals AI run leaderboard |
| MMLU-Pro (Vals) MMLU-Pro, Vals AI run | 88.6 | Academic multiple-choice questions | Broad academic knowledge | Vals AI MMLU-Pro, Vals AI run leaderboard |
Coding
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| Terminal-Bench 2.1 Terminal-Bench 2.1 (provider run) | 86.6 | Terminal-based software-agent tasks | Professional software engineering | DeepSeek V4 Flash 0731 update |
| SWE-bench Pro SWE-bench Pro | 67.7 | 1,865 repository problems | Long-horizon professional engineering | SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks? |
| VulcanBench v3 VulcanBench v3 | 81.2 | 23 post-cutoff repository tasks in the v3 report | Professional multi-file software engineering | VulcanBench |
| OpenHarmony Bench OpenHarmony Bench v1.0 | 60.8 | 153 app-development and bug-fix tasks | End-to-end OpenHarmony application development | OpenHarmony Bench: Evaluating LLMs and Coding Agents on OpenHarmony App Development |
| FrontierSWE FrontierSWE | 73.5 | 17 ultra-long-horizon engineering and research tasks | Ultra-long-horizon frontier software engineering | FrontierSWE: Benchmarking coding agents at the limits of human abilities |
| FrontierSWE v2 FrontierSWE v2 | 15.8 | 34 ultra-long-horizon engineering and research tasks | Ultra-long-horizon frontier software engineering | FrontierSWE v2 |
| MLS-Bench Lite MLS-Bench Lite | 41.0 | 30 machine-learning research tasks | ML research and systems engineering | MLS-Bench |
| PaperBench PaperBench | 93.0 | AI research-paper reproduction | Frontier autonomous research and engineering | Qwen3.8-Max: A New Bar for Coding and Cowork |
| QwenReactBench QwenReactBench | 1724 | Bilingual React project construction | Production frontend development | Qwen3.8-Max: A New Bar for Coding and Cowork |
| NL2Repo NL2Repo | 55.9 | Natural language to repository tasks | System-level software comprehension | MiniMax M2.7: Early Echoes of Self-Evolution |
| LiveCodeBench (Vals) LiveCodeBench, Vals AI run | 87.9 | Competitive programming problems (easy, medium, hard) | Frontier coding | Vals AI LiveCodeBench, Vals AI run leaderboard |
| SWE-bench (Vals) SWE-bench, Vals AI run | 85.6 | Real repository issues by human time bucket | Frontier coding agents | Vals AI SWE-bench, Vals AI run leaderboard |
| DeepSWE DeepSWE | 56.6 | 113 software engineering tasks across 91 repositories and 5 languages | Long-horizon software engineering | DeepSWE benchmark blog |
Reasoning
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| LongBench v2 LongBench v2 | 66.3 | Long-context tasks | Hard long-context | LongBench v2 |
| MRCRv2 MRCRv2 | 92.9 | Long-context retrieval | Hard long-context | Introducing GPT-5.2 and GPT-5.2 Pro |
Instruction Following
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| IFBench Instruction Following Benchmark | 82.8 | — | — | BenchLM |
Agentic
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| Terminal-Bench 2.1 Terminal-Bench 2.1 (provider run) | 86.6 | Terminal-based software-agent tasks | Professional software engineering | DeepSeek V4 Flash 0731 update |
| HLE w/ tools Humanity's Last Exam with tools | 56.2 | Expert questions with tool use | Frontier tool-augmented reasoning | DeepSeek-V4 Technical Report |
| OSWorld-Verified OSWorld-Verified | 86.1 | 369 real-world computer tasks (361 when eight Google Drive tasks are excluded) | Multi-step desktop and cross-application workflows | OSWorld |
| OSWorld 2.0 OSWorld 2.0 | 19.4 | 108 long-horizon computer-use workflows | Long-horizon professional workflows | OSWorld2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks |
| JobBench JobBench | 53.4 | 130 tasks across 35 occupations | Professional multi-source workflows | JobBench: Aligning Agent Work With Human Will |
| AndroidWorld AndroidWorld | 85.3 | Android app workflows | Complex mobile task completion | GLM-5V-Turbo |
| Toolathlon-Verified Toolathlon-Verified | 72.5 | Verified multi-tool workflows | Advanced tool use | Kimi K3: Open Frontier Intelligence |
| AutomationBench AutomationBench | 27.3 | 600 public automation tasks | Long-horizon automation | Kimi K3: Open Frontier Intelligence |
| Agents' Last Exam Agents' Last Exam | 52.4 | Agent tasks | Advanced agentic work | DeepSeek V4 Flash 0731 update |
| WideResearch WideResearch | 81.9 | Open-ended research tasks | Broad research-agent workflows | Qwen3.6 launch benchmarks |
| CoWorkBench CoWorkBench | 74.8 | Long-horizon professional workflows | Cross-domain professional work | Qwen3.8-Max: A New Bar for Coding and Cowork |
| MobileWorld MobileWorld | 77.8 | Interactive mobile-device workflows | Long-horizon mobile computer use | Qwen3.8-Max: A New Bar for Coding and Cowork |
| WebArena-Verified WebArena-Verified Browser Agent Benchmark | 66.8 | 812 verified tasks; separate 258-task Hard subset | Audited stateful browser work | WebArena-Verified: A Fully Audited Benchmark for Web Agents |
| Terminal-Bench 2.1 (Vals) Terminal-Bench 2.1, Vals AI run | 67.4 | Difficult terminal tasks | Frontier agentic | Vals AI Terminal-Bench 2.1, Vals AI run leaderboard |
Multimodal & Grounded
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| MMMU-Pro Massive Multi-discipline Multimodal Understanding Pro | 82.3 | Multimodal academic reasoning | Frontier multimodal | MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark |
| OCRBench V2 OCRBench V2 | 74.2 | Image OCR tasks | Native visual text understanding | OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning |
| MathVision w/ Python MathVision with Python | 97.7 | Visual mathematics problems with Python | Advanced multimodal mathematics | Kimi K3: Open Frontier Intelligence |
| BabyVision w/ Python BabyVision with Python | 91.3 | Visual perception tasks with Python | Fine-grained visual perception | Kimi K3: Open Frontier Intelligence |
| ZeroBench w/ Python ZeroBench_main with Python | 49.0 | Visual reasoning questions with Python | Tool-augmented visual reasoning | Kimi K3: Open Frontier Intelligence |
| PerceptionBench PerceptionBench (Internal) | 63.5 | Internal atomic visual-perception tasks | Fine-grained visual perception | Kimi K3: Open Frontier Intelligence |
| OmniDocBench 1.5 OmniDocBench 1.5 | 92.1 | Document understanding tasks | Grounded document reasoning | Introducing GPT-5.4 mini and nano |
| RealWorldQA RealWorldQA | 88.0 | Real-world visual question answering | General visual reasoning | Qwen3.6 launch benchmarks |
| Video-MME (with subtitle) Video-MME with subtitle | 90.4 | Video understanding | Multimodal video reasoning | Qwen3.6 launch benchmarks |
| MathVision MathVision | 95.2 | Visually grounded math problems | Advanced multimodal mathematics | Qwen3.6 launch benchmarks |
| CC-OCR CC-OCR | 79.6 | Optical character recognition | Document reading | Qwen3.6 launch benchmarks |
| ERQA ERQA | 77.8 | Evidence-based visual QA | Grounded multimodal reasoning | Qwen3.6 launch benchmarks |
| VideoMMMU VideoMMMU | 88.7 | Video-grounded expert reasoning | Frontier multimodal video reasoning | Qwen3.6 launch benchmarks |
| MLVU (M-Avg) MLVU mean average | 90.8 | General video understanding | Broad multimodal video reasoning | Qwen3.6 launch benchmarks |
| LVBench LVBench | 81.8 | Long-form video question answering | Extended temporal reasoning | Qwen3.8-Max: A New Bar for Coding and Cowork |
| MMVU Multimodal Multi-disciplinary Video Understanding | 82.4 | Video understanding | Multi-disciplinary multimodal video reasoning | Kimi K2.5 benchmark release surface |
| ScreenSpot Pro ScreenSpot Pro | 84.5 | 1,581 grounding instructions | Professional GUI grounding | ScreenSpot-Pro: GUI Grounding for Professional High-Resolution Computer Use |
| MedXpertQA (MM) MedXpertQA Multimodal | 80.4 | 2,000 multimodal medical questions | Clinical multimodal reasoning | Muse Spark Eval Methodology |
| ZeroBench ZeroBench | 24.0 | 100 visual reasoning questions | Tool-augmented visual reasoning | Muse Spark Eval Methodology |
| Vision2Web Vision2Web | 69.0 | Screenshot-to-web tasks | Multimodal web generation | GLM-5V-Turbo |
| SimpleVQA SimpleVQA | 75.0 | Visual QA tasks | General visual understanding | GLM-5V-Turbo |
| CharXiv CharXiv Reasoning | 93.5 | Scientific chart reasoning | Scientific visualization reasoning | CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs |
| CharXiv w/o tools CharXiv Reasoning without tools | 88.4 | Scientific chart reasoning (tool-free) | Scientific visualization reasoning | CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs |
| BabyVision BabyVision | 82.0 | Visual perception tasks | Fine-grained visual perception | Muse Spark 1.1 Evaluation Report |
Lifecycle and limitations log
Lifecycle events the source associates with this model, plus what this profile does not claim.
No lifecycle event references this model.
That is not evidence the model has no lifecycle plan — only that this source published none.
What this profile does not claimValues are reproduced exactly as their sources published them, in the units those sources declared; none are converted, interpolated, or averaged across providers. Any field marked unavailable was attempted and not returned — the attempt timestamp is in each badge. Last attempted fetch for this model’s score: 2026-09-17 17:17 UTC.