# Qwen3.7 Max

Alibaba · Proprietary · rank 29 · bench-align-v5

> Every figure below is reproduced as its upstream source published it: nothing is modelled, estimated, interpolated or converted. `Unavailable` means no source published the value — it is never a zero. Each value carries its evidence state and the date it was observed.

Page: https://modelscale.dev/models/qwen3-7-max  
JSON: https://modelscale.dev/api/model/qwen3-7-max

## Facts

| Field | Value | Evidence |
| --- | --- | --- |
| Canonical id | `qwen3-7-max` | — |
| Overall score | 66.98 | Observed 2026-09-22 · source benchlm:models |
| Context window | 1M tokens | Observed 2026-09-22 · source benchlm:models |
| Release date | 2026-05-16 | Observed 2026-09-22 · source benchlm:models |
| Access type | Proprietary | — |
| Blended $/1M (75% input / 25% output) | Unavailable | Derived from the input and output rates below |

## Capability evidence

Seven axes from the ranking source. An axis the source did not score is unavailable, not zero.

| Axis | Score | Evidence |
| --- | --- | --- |
| Agentic | 63.8 | Observed 2026-09-22 · source benchlm:models |
| Coding | 70.1 | Observed 2026-09-22 · source benchlm:models |
| Knowledge | 70.7 | Observed 2026-09-22 · source benchlm:models |
| Reasoning | 75.1 | Observed 2026-09-22 · source benchlm:models |
| Multimodal & Grounded | Unavailable | Unavailable · source benchlm:models |
| Instruction Following | 89.2 | Observed 2026-09-22 · source benchlm:models |
| Math | 81.9 | Observed 2026-09-22 · source benchlm:models |

## Runtime service evidence

Measured values with the date they were observed. Nothing is inferred from a sibling model or a provider claim. Regional or per-endpoint measurements appear only when the API supplies them; none are modelled.

| Measurement | Value | Observed | Last good | Evidence |
| --- | --- | --- | --- | --- |
| Time to first token | 14.48 s | 2026-09-22 | 2026-09-22 | Observed 2026-09-22 · source benchlm:speed |
| Throughput | 199 tok/s | 2026-09-22 | 2026-09-22 | Observed 2026-09-22 · source benchlm:speed |

## Endpoint and price matrix

Every published price component, including cache reads and writes.

| Component | USD | Evidence |
| --- | --- | --- |
| Input / 1M tokens | Unavailable | Unavailable · source benchlm:pricing |
| Output / 1M tokens | Unavailable | Unavailable · source benchlm:pricing |
| Cache read / 1M tokens | Unavailable | Unavailable · source benchlm:pricing |
| Cache write / 1M tokens | Unavailable | Unavailable · source openrouter:pricing |
| Blended / 1M (75% input / 25% output) | Unavailable | Derived — from the input and output rates above; it has no source record of its own |
| Cost per successful task (LiveBench) | Unavailable | Unavailable · source livebench:table |

**Self-hosted listing.** Qwen'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. Derived here from the published rates above by this site's own calculator — not a figure any source published.

| Field | Value |
| --- | --- |
| Modelled monthly cost | Unavailable |
| Modelled tokens | Unavailable |
| Reason | The applicable input rate is unavailable. |

## 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](https://arxiv.org/abs/2311.12022) |
| GPQA-D (GPQA Diamond) | 92.4 | Graduate-level science questions | Graduate level | [Trinity-Large-Thinking: Scaling an Open Source Frontier Agent](https://www.arcee.ai/blog/trinity-large-thinking) |
| SuperGPQA (SuperGPQA: Scaling LLM Evaluation Across 285 Graduate Disciplines) | 73.6 | 285 disciplines | Graduate level | [SuperGPQA: Scaling LLM Evaluation Across 285 Graduate Disciplines](https://arxiv.org/abs/2502.14739) |
| 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](https://arxiv.org/abs/2406.01574) |
| HLE (Humanity's Last Exam) | 41.4 | Expert-level questions | Frontier expert level | [Humanity's Last Exam](https://lastexam.ai/) |
| Artificial Analysis Intelligence Index | 29.5 | Cross-benchmark intelligence index | Display-only external reference | [Artificial Analysis](https://artificialanalysis.ai/) |
| AA-GPQA Diamond (Artificial Analysis GPQA Diamond) | 92.3 | Graduate-level science questions | Graduate-level science reasoning | [Artificial Analysis GPQA Diamond Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/gpqa-diamond) |
| AA-HLE (Artificial Analysis Humanity's Last Exam) | 40.5 | Expert-level questions | Frontier expert reasoning | [Artificial Analysis Humanity's Last Exam Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/hle) |
| AA-Omniscience Index (Artificial Analysis Omniscience Index) | 13.5 | Knowledge questions | Broad factual knowledge | [AA-Omniscience: Knowledge and Hallucination Benchmark](https://artificialanalysis.ai/evaluations/omniscience) |
| AA-Omniscience Accuracy (Artificial Analysis Omniscience Accuracy) | 31.1 | Knowledge questions | Broad knowledge | [Artificial Analysis model benchmarks](https://artificialanalysis.ai/models/grok-4-3) |
| AA-Omniscience Hallucination Rate (Artificial Analysis Omniscience Hallucination Rate) | 25.6 | Knowledge questions | Factuality | [Artificial Analysis model benchmarks](https://artificialanalysis.ai/models/grok-4-3) |
| MMLU-Redux | 95 | Broad academic QA | Advanced general knowledge | [Qwen3.6 launch benchmarks](https://qwen.ai/blog?id=qwen3.6) |
| MMMLU | 90.3 | Multilingual academic QA | Broad multilingual knowledge | [MMMLU](https://huggingface.co/datasets/openai/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](https://www.vals.ai/benchmarks/gpqa) |
| 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](https://www.vals.ai/benchmarks/mmlu_pro) |

### Coding

| Benchmark | Value | Tasks | Difficulty | Provenance |
| --- | --- | --- | --- | --- |
| Terminal-Bench 2.0 | 69.7 | Terminal-based software tasks | Professional software engineering | [Terminal-Bench 2.0](https://www.tbench.ai/) |
| 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?](https://arxiv.org/abs/2310.06770) |
| 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](https://arxiv.org/abs/2403.07974) |
| 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?](https://arxiv.org/abs/2509.16941) |
| 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](https://arxiv.org/abs/2608.16022) |
| SWE Multilingual | 78.3 | Multilingual software-engineering tasks | Professional software engineering | [MiniMax M2.7: Early Echoes of Self-Evolution](https://www.minimax.io/news/minimax-m27-en) |
| NL2Repo | 47.2 | Natural language to repository tasks | System-level software comprehension | [MiniMax M2.7: Early Echoes of Self-Evolution](https://www.minimax.io/news/minimax-m27-en) |
| SciCode (Scientific Code Benchmark) | 53.5 | — | — | [BenchLM](https://benchlm.ai/benchmarks/scicode) |
| AA Coding Index (Artificial Analysis Coding Index) | 66.0 | Cross-benchmark coding index | Display-only external reference | [Artificial Analysis model leaderboards](https://artificialanalysis.ai/leaderboards/models) |
| AA-SciCode (Artificial Analysis SciCode) | 49.5 | Scientific coding subproblems | Scientific programming | [Artificial Analysis SciCode Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/scicode) |
| LiveCodeBench (Vals) (LiveCodeBench, Vals AI run) | 87.1 | Competitive programming problems (easy, medium, hard) | Frontier coding | [Vals AI LiveCodeBench, Vals AI run leaderboard](https://www.vals.ai/benchmarks/lcb) |
| 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](https://www.vals.ai/benchmarks/swebench) |

### 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](https://qwen.ai/blog?id=qwen3.6) |
| IMOAnswerBench | 90.0 | Advanced mathematical answer generation | Olympiad-level mathematics | [DeepSeek-V4 Technical Report](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main/DeepSeek_V4.pdf) |
| Apex | 44.5 | Advanced mathematical reasoning | Frontier math reasoning | [DeepSeek-V4 Technical Report](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main/DeepSeek_V4.pdf) |

### Reasoning

| Benchmark | Value | Tasks | Difficulty | Provenance |
| --- | --- | --- | --- | --- |
| MRCRv2 | 90.4 | Long-context retrieval | Hard long-context | [Introducing GPT-5.2 and GPT-5.2 Pro](https://openai.com/index/introducing-gpt-5-2/) |
| AA-LCR (Artificial Analysis Long Context Reasoning) | 79.0 | Long-context reasoning tasks | Long-context reasoning | [Artificial Analysis model benchmarks](https://artificialanalysis.ai/models/grok-4-3) |
| CritPt (Critical Physics Tasks) | 13.4 | Research-level physics questions | Research-level physics reasoning | [CritPt Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/critpt) |

### 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](https://arxiv.org/abs/2311.07911) |
| IFBench (Instruction Following Benchmark) | 79.1 | — | — | [BenchLM](https://benchlm.ai/benchmarks/ifbench) |
| AA-IFBench (Artificial Analysis IFBench) | 80.5 | Verifiable instruction constraints | Instruction precision | [Artificial Analysis IFBench Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/ifbench) |

### Multilingual

| Benchmark | Value | Tasks | Difficulty | Provenance |
| --- | --- | --- | --- | --- |
| MMLU-ProX | 87 | Multilingual professional QA | Professional multilingual | [MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation](https://arxiv.org/abs/2503.10497) |
| NOVA-63 | 59.0 | Broad multilingual evaluation | Broad multilingual capability | [Qwen3.6 launch benchmarks](https://qwen.ai/blog?id=qwen3.6) |
| INCLUDE | 86.2 | Cross-lingual understanding | Broad multilingual capability | [Qwen3.6 launch benchmarks](https://qwen.ai/blog?id=qwen3.6) |
| PolyMath | 86.5 | Multilingual math problems | Advanced multilingual reasoning | [Qwen3.6 launch benchmarks](https://qwen.ai/blog?id=qwen3.6) |
| MAXIFE | 89.2 | Multilingual instruction following | Advanced multilingual instruction following | [Qwen3.6 launch benchmarks](https://qwen.ai/blog?id=qwen3.6) |

### 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](https://artificialanalysis.ai/evaluations/itbench-aa) |
| Terminal-Bench 2.0 | 69.7 | Terminal-based software tasks | Professional software engineering | [Terminal-Bench 2.0](https://www.tbench.ai/) |
| HLE w/ tools (Humanity's Last Exam with tools) | 53.5 | Expert questions with tool use | Frontier tool-augmented reasoning | [DeepSeek-V4 Technical Report](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main/DeepSeek_V4.pdf) |
| GDPval-AA | 1190 | Agentic real-world work tasks | Professional agentic workflows | [DeepSeek-V4 Technical Report](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main/DeepSeek_V4.pdf) |
| GDPval-AA (GDPval-AA normalized) | 30.7 | Economically valuable tasks | Professional agentic workflows | [Artificial Analysis model benchmarks](https://artificialanalysis.ai/models/grok-4-3) |
| AA Agentic Index (Artificial Analysis Agentic Index) | 23.9 | Cross-benchmark agentic index | Display-only external reference | [Artificial Analysis model leaderboards](https://artificialanalysis.ai/leaderboards/models) |
| Gert Labs (Gert Labs Composite Game Benchmark) | 64.27 | Novel game environments | Agentic coding and decision-making | [Gert Labs rankings](https://gertlabs.com/rankings) |
| MCP Atlas | 76.4 | Tool-integrated agent tasks | Advanced tool use | [Introducing GPT-5.4 mini and nano](https://openai.com/index/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](https://arxiv.org/abs/2506.07982) |
| BFCL v4 (Berkeley Function Calling Leaderboard v4) | 75.0 | Function-calling tasks | Advanced tool use | [Trinity-Large-Thinking: Scaling an Open Source Frontier Agent](https://www.arcee.ai/blog/trinity-large-thinking) |
| 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](https://arxiv.org/abs/2604.06132) |
| ResearchClawBench | 18.7 | 40 tasks across 10 scientific domains | Scientific research re-discovery | [ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research](https://arxiv.org/abs/2606.07591) |
| QwenClawBench | 64.3 | Real-world agent workflows | Broad real-world agentic execution | [Qwen3.6 launch benchmarks](https://qwen.ai/blog?id=qwen3.6) |
| QwenWebBench | 1568 | Web artifacts and interactive deliverables | Artifact generation | [Qwen3.6 launch benchmarks](https://qwen.ai/blog?id=qwen3.6) |
| 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](https://vitabench.github.io/) |
| 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](https://www.vals.ai/benchmarks/terminal-bench-2-1) |

### Multimodal & Grounded

| Benchmark | Value | Tasks | Difficulty | Provenance |
| --- | --- | --- | --- | --- |
| Design Arena Website (Design Arena Website Elo) | 1284 | Website generation comparisons | Design and website generation | [OpenRouter Grok 4.3 benchmarks](https://openrouter.ai/x-ai/grok-4.3/benchmarks) |

## Lifecycle and limitations log

Lifecycle events the source associates with this model.

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 claim

Values 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. Last attempted fetch for this model's score: 2026-09-22 10:17 UTC.