Qwen3.5 397B
Alibaba · Open Weight · rank 92 · bench-align-v5
Canonical idqwen3-5-397b
Access typeOpen Weight
Blended $/1M$1.3575% 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 | 39.78 s | 2026-09-17 | 2026-09-17 | |
| Throughput | 85 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.
| Component | USD | Evidence |
|---|---|---|
| Input / 1M tokens | $0.60 | |
| Output / 1M tokens | $3.60 | |
| Cache read / 1M tokens | Unavailable | |
| Cache write / 1M tokens | Unavailable | |
| Blended / 1M (75% input / 25% output) | $1.35 |
Self-hosted listingAlibaba Cloud Model Studio's pricing page lists qwen3.5-397b-a17b at $0.60 input / $3.60 output per million tokens in International deployment mode for prompts up to 256K tokens.The rates above are a hosted price matched from another provider, not a first-party list price.
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.
Benchmark record
49 matched benchmark rows with their published value, unit, and provenance.
Knowledge
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| GPQA Graduate-Level Google-Proof Q&A | 88.4 | 448 questions | Graduate level | GPQA: A Graduate-Level Google-Proof Q&A Benchmark |
| SuperGPQA SuperGPQA: Scaling LLM Evaluation Across 285 Graduate Disciplines | 70.4 | 285 disciplines | Graduate level | SuperGPQA: Scaling LLM Evaluation Across 285 Graduate Disciplines |
| MMLU-Pro Massive Multitask Language Understanding Professional | 87.8 | Multiple subjects | Professional level | MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark |
| HLE Humanity's Last Exam | 28.7 | Expert-level questions | Frontier expert level | Humanity's Last Exam |
| Artificial Analysis Intelligence Index Artificial Analysis Intelligence Index | 21.4 | Cross-benchmark intelligence index | Display-only external reference | Artificial Analysis |
| AA-GPQA Diamond Artificial Analysis GPQA Diamond | 86.1 | Graduate-level science questions | Graduate-level science reasoning | Artificial Analysis GPQA Diamond Benchmark Leaderboard |
| AA-HLE Artificial Analysis Humanity's Last Exam | 19.8 | Expert-level questions | Frontier expert reasoning | Artificial Analysis Humanity's Last Exam Benchmark Leaderboard |
| AA-Omniscience Index Artificial Analysis Omniscience Index | -37.9 | Knowledge questions | Broad factual knowledge | AA-Omniscience: Knowledge and Hallucination Benchmark |
| AA-Omniscience Accuracy Artificial Analysis Omniscience Accuracy | 24.5 | Knowledge questions | Broad knowledge | Artificial Analysis model benchmarks |
| AA-Omniscience Hallucination Rate Artificial Analysis Omniscience Hallucination Rate | 82.7 | Knowledge questions | Factuality | Artificial Analysis model benchmarks |
| MMLU-Redux MMLU-Redux | 94.9 | Broad academic QA | Advanced general knowledge | Qwen3.6 launch benchmarks |
| C-Eval C-Eval | 93 | Chinese academic and professional exams | High school to professional level | C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models |
Coding
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| SWE-bench Verified Software Engineering Benchmark Verified | 76.2 | 500 verified issues | Professional software engineering | SWE-bench: Can Language Models Resolve Real-World GitHub Issues? |
| LiveCodeBench v6 LiveCodeBench v6 | 83.6 | Fresh programming problems | Competitive programming level | LiveCodeBench official repository and release documentation |
| SWE-bench Pro SWE-bench Pro | 50.9 | 1,865 repository problems | Long-horizon professional engineering | SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks? |
Mathematics
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| AIME26 AIME 2026 | 93.3 | Competition math problems | Olympiad-style mathematics | Qwen3.6 launch benchmarks |
| HMMT Feb 2025 Harvard-MIT Mathematics Tournament February 2025 | 94.8 | Competition math problems | Olympiad-style mathematics | Qwen3.6 launch benchmarks |
| HMMT Nov 2025 Harvard-MIT Mathematics Tournament November 2025 | 92.7 | Competition math problems | Olympiad-style mathematics | Qwen3.6 launch benchmarks |
| HMMT Feb 2026 Harvard-MIT Mathematics Tournament February 2026 | 87.9 | Competition math problems | Olympiad-style mathematics | Qwen3.6 launch benchmarks |
| MMAnswerBench MMAnswerBench | 80.9 | Multimodal math questions | Advanced mathematical reasoning | Qwen3.6 launch benchmarks |
Reasoning
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| LongBench v2 LongBench v2 | 63.2 | Long-context tasks | Hard long-context | LongBench v2 |
| AI-Needle AI-Needle | 68.7 | Long-context retrieval | Long-context memory | Qwen3.6 launch benchmarks |
| AA-LCR Artificial Analysis Long Context Reasoning | 64.3 | Long-context reasoning tasks | Long-context reasoning | Artificial Analysis model benchmarks |
| CritPt Critical Physics Tasks | 0.9 | Research-level physics questions | Research-level physics reasoning | CritPt Benchmark Leaderboard |
Instruction Following
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| IFEval Instruction-Following Eval | 92.6 | 541 prompts across 25 instruction types | Instruction precision | Instruction-Following Evaluation for Large Language Models |
| AA-IFBench Artificial Analysis IFBench | 51.6 | Verifiable instruction constraints | Instruction precision | Artificial Analysis IFBench Benchmark Leaderboard |
Multilingual
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| MMLU-ProX MMLU-ProX | 84.7 | Multilingual professional QA | Professional multilingual | MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation |
| NOVA-63 NOVA-63 | 59.1 | Broad multilingual evaluation | Broad multilingual capability | Qwen3.6 launch benchmarks |
Agentic
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| Terminal-Bench 2.0 Terminal-Bench 2.0 | 52.5 | Terminal-based software tasks | Professional software engineering | Terminal-Bench 2.0 |
| BrowseComp BrowseComp | 62 | Research questions requiring browsing | Hard web research | BrowseComp |
| Gert Labs Gert Labs Composite Game Benchmark | 46.76 | Novel game environments | Agentic coding and decision-making | Gert Labs rankings |
| MCP Atlas MCP Atlas | 46.1 | Tool-integrated agent tasks | Advanced tool use | Introducing GPT-5.4 mini and nano |
| Toolathlon Toolathlon | 36.3 | Multi-tool workflows | Advanced tool use | Introducing GPT-5.4 mini and nano |
| τ²-bench results τ²-Bench Tool-Agent-User Evaluation | 83.9 | Airline, retail, and telecom customer-service task sets | Dual-control customer-service workflows | τ²-Bench: Evaluating Conversational Agents in a Dual-Control Environment |
| Claw-Eval Claw-Eval | 56.8 | 300 tasks, 2,159 rubrics | Real-world general, multi-turn, and native multimodal agent execution | Claw-Eval: Towards Trustworthy Evaluation of Autonomous Agents |
| ResearchClawBench ResearchClawBench | 14.2 | 40 tasks across 10 scientific domains | Scientific research re-discovery | ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research |
| QwenClawBench QwenClawBench | 51.8 | Real-world agent workflows | Broad real-world agentic execution | Qwen3.6 launch benchmarks |
| τ³-bench results τ³-Bench Tool-Agent-User Evaluation | 68.4 | Corrected customer-service tasks plus knowledge and voice evaluation modes | Long-horizon, multimodal, and knowledge-aware tool use | Official τ³-bench repository and release notes |
| VITA-Bench VITA-Bench | 43.7 | Interactive consumer-service agent tasks | Long-horizon real-world workflows | VitaBench: Benchmarking LLM Agents with Versatile Interactive Tasks in Real-world Applications |
| DeepPlanning DeepPlanning | 37.6 | Travel planning and constrained shopping | Constrained agent planning | DeepPlanning: Benchmarking Long-Horizon Agentic Planning with Verifiable Constraints |
| MCP-Tasks MCP-Tasks | 74.2 | MCP-integrated tool tasks | Advanced MCP workflows | Qwen3.6 launch benchmarks |
| WideResearch WideResearch | 74.0 | Open-ended research tasks | Broad research-agent workflows | Qwen3.6 launch benchmarks |
Multimodal & Grounded
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| MMMU-Pro Massive Multi-discipline Multimodal Understanding Pro | 79 | Multimodal academic reasoning | Frontier multimodal | MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark |
| AA-MMMU-Pro Artificial Analysis MMMU-Pro | 52.7 | Multimodal academic reasoning | Frontier multimodal | Artificial Analysis MMMU-Pro Benchmark Leaderboard |
| MathVision MathVision | 88.6 | Visually grounded math problems | Advanced multimodal mathematics | Qwen3.6 launch benchmarks |
| VideoMMMU VideoMMMU | 84.7 | Video-grounded expert reasoning | Frontier multimodal video reasoning | Qwen3.6 launch benchmarks |
| ScreenSpot Pro ScreenSpot Pro | 65.6 | 1,581 grounding instructions | Professional GUI grounding | ScreenSpot-Pro: GUI Grounding for Professional High-Resolution Computer Use |
| V* V* | 95.8 | Frontier multimodal reasoning tasks | Frontier multimodal | GLM-5V-Turbo |
| CharXiv CharXiv Reasoning | 80.8 | Scientific chart reasoning | Scientific visualization reasoning | CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs |
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.