Qwen3.7 Plus
Alibaba · Proprietary · rank 50 · bench-align-v5
Canonical idqwen3-7-plus
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 | 30.66 s | 2026-09-17 | 2026-09-17 | |
| Throughput | 70 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.
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
70 matched benchmark rows with their published value, unit, and provenance.
Knowledge
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| GPQA Graduate-Level Google-Proof Q&A | 90.3 | 448 questions | Graduate level | GPQA: A Graduate-Level Google-Proof Q&A Benchmark |
| GPQA-D GPQA Diamond | 90.3 | Graduate-level science questions | Graduate level | Trinity-Large-Thinking: Scaling an Open Source Frontier Agent |
| SuperGPQA SuperGPQA: Scaling LLM Evaluation Across 285 Graduate Disciplines | 71.4 | 285 disciplines | Graduate level | SuperGPQA: Scaling LLM Evaluation Across 285 Graduate Disciplines |
| MMLU-Pro Massive Multitask Language Understanding Professional | 88.5 | Multiple subjects | Professional level | MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark |
| HLE Humanity's Last Exam | 34.7 | Expert-level questions | Frontier expert level | Humanity's Last Exam |
| Artificial Analysis Intelligence Index Artificial Analysis Intelligence Index | 25.8 | Cross-benchmark intelligence index | Display-only external reference | Artificial Analysis |
| AA-GPQA Diamond Artificial Analysis GPQA Diamond | 90.0 | Graduate-level science questions | Graduate-level science reasoning | Artificial Analysis GPQA Diamond Benchmark Leaderboard |
| AA-HLE Artificial Analysis Humanity's Last Exam | 35.6 | Expert-level questions | Frontier expert reasoning | Artificial Analysis Humanity's Last Exam Benchmark Leaderboard |
| AA-Omniscience Index Artificial Analysis Omniscience Index | 1.1 | Knowledge questions | Broad factual knowledge | AA-Omniscience: Knowledge and Hallucination Benchmark |
| AA-Omniscience Accuracy Artificial Analysis Omniscience Accuracy | 22.5 | Knowledge questions | Broad knowledge | Artificial Analysis model benchmarks |
| AA-Omniscience Hallucination Rate Artificial Analysis Omniscience Hallucination Rate | 27.7 | Knowledge questions | Factuality | Artificial Analysis model benchmarks |
| MMLU-Redux MMLU-Redux | 94.5 | Broad academic QA | Advanced general knowledge | Qwen3.6 launch benchmarks |
| MMMLU MMMLU | 89.0 | Multilingual academic QA | Broad multilingual knowledge | MMMLU |
Coding
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| Terminal-Bench 2.0 Terminal-Bench 2.0 | 70.3 | Terminal-based software tasks | Professional software engineering | Terminal-Bench 2.0 |
| SWE-bench Verified Software Engineering Benchmark Verified | 77.7 | 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 | 89.6 | 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 | 57.6 | 1,865 repository problems | Long-horizon professional engineering | SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks? |
| SWE Multilingual SWE Multilingual | 75.8 | Multilingual software-engineering tasks | Professional software engineering | MiniMax M2.7: Early Echoes of Self-Evolution |
| NL2Repo NL2Repo | 41.1 | Natural language to repository tasks | System-level software comprehension | MiniMax M2.7: Early Echoes of Self-Evolution |
| SciCode Scientific Code Benchmark | 51.3 | — | — | BenchLM |
| AA Coding Index Artificial Analysis Coding Index | 55.9 | Cross-benchmark coding index | Display-only external reference | Artificial Analysis model leaderboards |
| AA-SciCode Artificial Analysis SciCode | 46.1 | Scientific coding subproblems | Scientific programming | Artificial Analysis SciCode Benchmark Leaderboard |
Mathematics
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| HMMT Feb 2026 Harvard-MIT Mathematics Tournament February 2026 | 92.9 | Competition math problems | Olympiad-style mathematics | Qwen3.6 launch benchmarks |
| IMOAnswerBench IMOAnswerBench | 86.0 | Advanced mathematical answer generation | Olympiad-level mathematics | DeepSeek-V4 Technical Report |
| Apex Apex | 22.7 | Advanced mathematical reasoning | Frontier math reasoning | DeepSeek-V4 Technical Report |
Reasoning
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| MRCRv2 MRCRv2 | 91.7 | Long-context retrieval | Hard long-context | Introducing GPT-5.2 and GPT-5.2 Pro |
| AA-LCR Artificial Analysis Long Context Reasoning | 73.0 | Long-context reasoning tasks | Long-context reasoning | Artificial Analysis model benchmarks |
| CritPt Critical Physics Tasks | 9.1 | Research-level physics questions | Research-level physics reasoning | CritPt Benchmark Leaderboard |
Instruction Following
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| IFEval Instruction-Following Eval | 94.6 | 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 | 78.0 | Verifiable instruction constraints | Instruction precision | Artificial Analysis IFBench Benchmark Leaderboard |
Multilingual
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| MMLU-ProX MMLU-ProX | 85.4 | Multilingual professional QA | Professional multilingual | MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation |
| NOVA-63 NOVA-63 | 58.8 | Broad multilingual evaluation | Broad multilingual capability | Qwen3.6 launch benchmarks |
| INCLUDE INCLUDE | 83.0 | Cross-lingual understanding | Broad multilingual capability | Qwen3.6 launch benchmarks |
| PolyMath PolyMath | 84.0 | Multilingual math problems | Advanced multilingual reasoning | Qwen3.6 launch benchmarks |
| MAXIFE MAXIFE | 88.8 | Multilingual instruction following | Advanced multilingual instruction following | Qwen3.6 launch benchmarks |
Agentic
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| Terminal-Bench 2.0 Terminal-Bench 2.0 | 70.3 | Terminal-based software tasks | Professional software engineering | Terminal-Bench 2.0 |
| GDPval-AA GDPval-AA | 886 | Agentic real-world work tasks | Professional agentic workflows | DeepSeek-V4 Technical Report |
| GDPval-AA GDPval-AA normalized | 19.3 | Economically valuable tasks | Professional agentic workflows | Artificial Analysis model benchmarks |
| AA Agentic Index Artificial Analysis Agentic Index | 19.7 | Cross-benchmark agentic index | Display-only external reference | Artificial Analysis model leaderboards |
| APEX-Agents-AA APEX-Agents-AA | 22.4 | 452 professional-services agent tasks | Long-horizon workplace agent tasks | APEX-Agents-AA Benchmark Leaderboard |
| OSWorld-Verified OSWorld-Verified | 73.3 | 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 | 2.8 | 108 long-horizon computer-use workflows | Long-horizon professional workflows | OSWorld2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks |
| AndroidWorld AndroidWorld | 81.0 | Android app workflows | Complex mobile task completion | GLM-5V-Turbo |
| MCP Atlas MCP Atlas | 73.2 | Tool-integrated agent tasks | Advanced tool use | Introducing GPT-5.4 mini and nano |
| τ²-bench results τ²-Bench Tool-Agent-User Evaluation | 93 | 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 | 72.9 | Function-calling tasks | Advanced tool use | Trinity-Large-Thinking: Scaling an Open Source Frontier Agent |
| Claw-Eval Claw-Eval | 62.7 | 300 tasks, 2,159 rubrics | Real-world general, multi-turn, and native multimodal agent execution | Claw-Eval: Towards Trustworthy Evaluation of Autonomous Agents |
| QwenClawBench QwenClawBench | 61.8 | Real-world agent workflows | Broad real-world agentic execution | Qwen3.6 launch benchmarks |
| QwenWebBench QwenWebBench | 1536 | Web artifacts and interactive deliverables | Artifact generation | Qwen3.6 launch benchmarks |
| VITA-Bench VITA-Bench | 45.6 | Interactive consumer-service agent tasks | Long-horizon real-world workflows | VitaBench: Benchmarking LLM Agents with Versatile Interactive Tasks in Real-world Applications |
| DeepPlanning DeepPlanning | 62.3 | Travel planning and constrained shopping | Constrained agent planning | DeepPlanning: Benchmarking Long-Horizon Agentic Planning with Verifiable Constraints |
| Terminal-Bench 2.1 (Vals) Terminal-Bench 2.1, Vals AI run | 52.8 | 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 | 79 | Multimodal academic reasoning | Frontier multimodal | MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark |
| AA-MMMU-Pro Artificial Analysis MMMU-Pro | 80.5 | Multimodal academic reasoning | Frontier multimodal | Artificial Analysis MMMU-Pro Benchmark Leaderboard |
| OCRBench V2 OCRBench V2 | 70.7 | Image OCR tasks | Native visual text understanding | OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning |
| Design Arena Website Design Arena Website Elo | 1281 | Website generation comparisons | Design and website generation | OpenRouter Grok 4.3 benchmarks |
| OmniDocBench 1.5 OmniDocBench 1.5 | 91.4 | Document understanding tasks | Grounded document reasoning | Introducing GPT-5.4 mini and nano |
| RealWorldQA RealWorldQA | 86.9 | Real-world visual question answering | General visual reasoning | Qwen3.6 launch benchmarks |
| Video-MME (with subtitle) Video-MME with subtitle | 88.0 | Video understanding | Multimodal video reasoning | Qwen3.6 launch benchmarks |
| MathVision MathVision | 90.3 | Visually grounded math problems | Advanced multimodal mathematics | Qwen3.6 launch benchmarks |
| ODINW13 ODINW13 | 51.1 | Out-of-distribution object understanding | Robust visual grounding | Qwen3.6 launch benchmarks |
| ERQA ERQA | 69.8 | Evidence-based visual QA | Grounded multimodal reasoning | Qwen3.6 launch benchmarks |
| VideoMMMU VideoMMMU | 85.4 | Video-grounded expert reasoning | Frontier multimodal video reasoning | Qwen3.6 launch benchmarks |
| MLVU (M-Avg) MLVU mean average | 87.4 | General video understanding | Broad multimodal video reasoning | Qwen3.6 launch benchmarks |
| ScreenSpot Pro ScreenSpot Pro | 79.0 | 1,581 grounding instructions | Professional GUI grounding | ScreenSpot-Pro: GUI Grounding for Professional High-Resolution Computer Use |
| MedXpertQA (MM) MedXpertQA Multimodal | 71.0 | 2,000 multimodal medical questions | Clinical multimodal reasoning | Muse Spark Eval Methodology |
| MMSearch-Plus MMSearch-Plus | 41.4 | Hard multimodal search tasks | Advanced multimodal search | GLM-5V-Turbo |
| SimpleVQA SimpleVQA | 81.7 | Visual QA tasks | General visual understanding | GLM-5V-Turbo |
| CharXiv CharXiv Reasoning | 85.9 | 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.