Qwen3.7 Max
Alibaba · Proprietary · rank 29 · bench-align-v5
Canonical idqwen3-7-max
Access typeProprietary
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 | 14.54 s | 2026-09-17 | 2026-09-17 | |
| Throughput | 201 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's May 16, 2026 launch post says qwen3.7-max will be available soon through Alibaba Cloud Model Studio and shows API usage, but does not publish exact token pricing.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
58 matched benchmark rows with their published value, unit, and provenance.
Knowledge
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| GPQA Graduate-Level Google-Proof Q&A | 92.4 | 448 questions | Graduate level | GPQA: A Graduate-Level Google-Proof Q&A Benchmark |
| GPQA-D GPQA Diamond | 92.4 | Graduate-level science questions | Graduate level | Trinity-Large-Thinking: Scaling an Open Source Frontier Agent |
| SuperGPQA SuperGPQA: Scaling LLM Evaluation Across 285 Graduate Disciplines | 73.6 | 285 disciplines | Graduate level | SuperGPQA: Scaling LLM Evaluation Across 285 Graduate Disciplines |
| MMLU-Pro Massive Multitask Language Understanding Professional | 89.6 | Multiple subjects | Professional level | MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark |
| HLE Humanity's Last Exam | 41.4 | Expert-level questions | Frontier expert level | Humanity's Last Exam |
| Artificial Analysis Intelligence Index Artificial Analysis Intelligence Index | 29.9 | Cross-benchmark intelligence index | Display-only external reference | Artificial Analysis |
| AA-GPQA Diamond Artificial Analysis GPQA Diamond | 92.3 | Graduate-level science questions | Graduate-level science reasoning | Artificial Analysis GPQA Diamond Benchmark Leaderboard |
| AA-HLE Artificial Analysis Humanity's Last Exam | 40.5 | Expert-level questions | Frontier expert reasoning | Artificial Analysis Humanity's Last Exam Benchmark Leaderboard |
| AA-Omniscience Index Artificial Analysis Omniscience Index | 13.5 | Knowledge questions | Broad factual knowledge | AA-Omniscience: Knowledge and Hallucination Benchmark |
| AA-Omniscience Accuracy Artificial Analysis Omniscience Accuracy | 31.1 | Knowledge questions | Broad knowledge | Artificial Analysis model benchmarks |
| AA-Omniscience Hallucination Rate Artificial Analysis Omniscience Hallucination Rate | 25.6 | Knowledge questions | Factuality | Artificial Analysis model benchmarks |
| MMLU-Redux MMLU-Redux | 95 | Broad academic QA | Advanced general knowledge | Qwen3.6 launch benchmarks |
| MMMLU MMMLU | 90.3 | Multilingual academic QA | Broad multilingual knowledge | MMMLU |
| GPQA Diamond (Vals) GPQA Diamond, Vals AI run | 90.2 | Graduate-level science questions | Expert reasoning | Vals AI GPQA Diamond, Vals AI run leaderboard |
| MMLU-Pro (Vals) MMLU-Pro, Vals AI run | 89.3 | Academic multiple-choice questions | Broad academic knowledge | Vals AI MMLU-Pro, Vals AI run leaderboard |
Coding
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| Terminal-Bench 2.0 Terminal-Bench 2.0 | 69.7 | Terminal-based software tasks | Professional software engineering | Terminal-Bench 2.0 |
| SWE-bench Verified Software Engineering Benchmark Verified | 80.4 | 500 verified issues | Professional software engineering | SWE-bench: Can Language Models Resolve Real-World GitHub Issues? |
| LiveCodeBench LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code | 91.6 | Continuously updated contest problems | Competitive programming level | LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code |
| SWE-bench Pro SWE-bench Pro | 60.6 | 1,865 repository problems | Long-horizon professional engineering | SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks? |
| OpenHarmony Bench OpenHarmony Bench v1.0 | 53.4 | 153 app-development and bug-fix tasks | End-to-end OpenHarmony application development | OpenHarmony Bench: Evaluating LLMs and Coding Agents on OpenHarmony App Development |
| SWE Multilingual SWE Multilingual | 78.3 | Multilingual software-engineering tasks | Professional software engineering | MiniMax M2.7: Early Echoes of Self-Evolution |
| NL2Repo NL2Repo | 47.2 | Natural language to repository tasks | System-level software comprehension | MiniMax M2.7: Early Echoes of Self-Evolution |
| SciCode Scientific Code Benchmark | 53.5 | — | — | BenchLM |
| AA Coding Index Artificial Analysis Coding Index | 66.0 | Cross-benchmark coding index | Display-only external reference | Artificial Analysis model leaderboards |
| AA-SciCode Artificial Analysis SciCode | 49.5 | Scientific coding subproblems | Scientific programming | Artificial Analysis SciCode Benchmark Leaderboard |
| LiveCodeBench (Vals) LiveCodeBench, Vals AI run | 87.1 | Competitive programming problems (easy, medium, hard) | Frontier coding | Vals AI LiveCodeBench, Vals AI run leaderboard |
| SWE-bench (Vals) SWE-bench, Vals AI run | 68.8 | Real repository issues by human time bucket | Frontier coding agents | Vals AI SWE-bench, Vals AI run leaderboard |
Mathematics
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| HMMT Feb 2026 Harvard-MIT Mathematics Tournament February 2026 | 97.1 | Competition math problems | Olympiad-style mathematics | Qwen3.6 launch benchmarks |
| IMOAnswerBench IMOAnswerBench | 90.0 | Advanced mathematical answer generation | Olympiad-level mathematics | DeepSeek-V4 Technical Report |
| Apex Apex | 44.5 | Advanced mathematical reasoning | Frontier math reasoning | DeepSeek-V4 Technical Report |
Reasoning
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| MRCRv2 MRCRv2 | 90.4 | Long-context retrieval | Hard long-context | Introducing GPT-5.2 and GPT-5.2 Pro |
| AA-LCR Artificial Analysis Long Context Reasoning | 79.0 | Long-context reasoning tasks | Long-context reasoning | Artificial Analysis model benchmarks |
| CritPt Critical Physics Tasks | 13.4 | Research-level physics questions | Research-level physics reasoning | CritPt Benchmark Leaderboard |
Instruction Following
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| IFEval Instruction-Following Eval | 94.3 | 541 prompts across 25 instruction types | Instruction precision | Instruction-Following Evaluation for Large Language Models |
| IFBench Instruction Following Benchmark | 79.1 | — | — | BenchLM |
| AA-IFBench Artificial Analysis IFBench | 80.5 | Verifiable instruction constraints | Instruction precision | Artificial Analysis IFBench Benchmark Leaderboard |
Multilingual
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| MMLU-ProX MMLU-ProX | 87 | Multilingual professional QA | Professional multilingual | MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation |
| NOVA-63 NOVA-63 | 59.0 | Broad multilingual evaluation | Broad multilingual capability | Qwen3.6 launch benchmarks |
| INCLUDE INCLUDE | 86.2 | Cross-lingual understanding | Broad multilingual capability | Qwen3.6 launch benchmarks |
| PolyMath PolyMath | 86.5 | Multilingual math problems | Advanced multilingual reasoning | Qwen3.6 launch benchmarks |
| MAXIFE MAXIFE | 89.2 | Multilingual instruction following | Advanced multilingual instruction following | Qwen3.6 launch benchmarks |
Agentic
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| AA ITBench Artificial Analysis ITBench-AA | 42.5 | IT incident-response tasks | Enterprise IT operations | Artificial Analysis ITBench-AA Benchmark Leaderboard |
| Terminal-Bench 2.0 Terminal-Bench 2.0 | 69.7 | Terminal-based software tasks | Professional software engineering | Terminal-Bench 2.0 |
| HLE w/ tools Humanity's Last Exam with tools | 53.5 | Expert questions with tool use | Frontier tool-augmented reasoning | DeepSeek-V4 Technical Report |
| GDPval-AA GDPval-AA | 1190 | Agentic real-world work tasks | Professional agentic workflows | DeepSeek-V4 Technical Report |
| GDPval-AA GDPval-AA normalized | 34.5 | Economically valuable tasks | Professional agentic workflows | Artificial Analysis model benchmarks |
| AA Agentic Index Artificial Analysis Agentic Index | 23.9 | Cross-benchmark agentic index | Display-only external reference | Artificial Analysis model leaderboards |
| Gert Labs Gert Labs Composite Game Benchmark | 64.27 | Novel game environments | Agentic coding and decision-making | Gert Labs rankings |
| MCP Atlas MCP Atlas | 76.4 | Tool-integrated agent tasks | Advanced tool use | Introducing GPT-5.4 mini and nano |
| τ²-bench results τ²-Bench Tool-Agent-User Evaluation | 94.7 | Airline, retail, and telecom customer-service task sets | Dual-control customer-service workflows | τ²-Bench: Evaluating Conversational Agents in a Dual-Control Environment |
| BFCL v4 Berkeley Function Calling Leaderboard v4 | 75.0 | Function-calling tasks | Advanced tool use | Trinity-Large-Thinking: Scaling an Open Source Frontier Agent |
| Claw-Eval Claw-Eval | 65.2 | 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 | 18.7 | 40 tasks across 10 scientific domains | Scientific research re-discovery | ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research |
| QwenClawBench QwenClawBench | 64.3 | Real-world agent workflows | Broad real-world agentic execution | Qwen3.6 launch benchmarks |
| QwenWebBench QwenWebBench | 1568 | Web artifacts and interactive deliverables | Artifact generation | Qwen3.6 launch benchmarks |
| VITA-Bench VITA-Bench | 47.9 | Interactive consumer-service agent tasks | Long-horizon real-world workflows | VitaBench: Benchmarking LLM Agents with Versatile Interactive Tasks in Real-world Applications |
| Terminal-Bench 2.1 (Vals) Terminal-Bench 2.1, Vals AI run | 61.0 | Difficult terminal tasks | Frontier agentic | Vals AI Terminal-Bench 2.1, Vals AI run leaderboard |
Multimodal & Grounded
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| Design Arena Website Design Arena Website Elo | 1285 | Website generation comparisons | Design and website generation | OpenRouter Grok 4.3 benchmarks |
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.