Qwen3.7-Max: Artificial Analysis Intelligence Index, Cost, and Speed Benchmark

One-sentence takeaway

Artificial Analysis independent benchmark results show Qwen3.7-Max scoring 47 on the Intelligence Index (top 23%) , ranking #9 with an output speed of 206.4 tok/s, and achieving a per-task cost of $0.54—significantly lower than Opus 5 and GPT-5.6 Sol—though its tendency to generate 100M tokens reflects noticeable long-thought verbosity.

Test environment

• Evaluation framework: Artificial Analysis Intelligence Index v4.1.1.

• Benchmark subsets: 9 controlled evaluations including GDPval-AA v2, tau^3-Banking (𝜏³-Banking) , Terminal-Bench v2.1, SciCode, Humanity's Last Exam (HLE) , GPQA Diamond, CritPt, AA-Omniscience, and AA-LCR.

• Inference environment: Tested on the official Alibaba Cloud Model Studio inference endpoint under a default 10k input token workload.

• Comparison pool: Cross-model comparison across 182 frontier reasoning and proprietary models worldwide.

Inputs/configuration

• Evaluation mode: Full Reasoning / Thinking mode enabled.

• Input/output modalities: Pure text (Text-only) , supporting up to a 1M token context per single request.

• Pricing baseline: Input $2.50 / 1M tokens, Output $7.50 / 1M tokens, with an 80% context caching discount.

• Total consumption: In completing the entire Intelligence Index benchmark suite, a cumulative 100M output tokens were generated, totaling $1,063.86 in testing expenses.

Results data

Core capabilities and performance rankings

Cross-model weighted cost per task comparison (Cost per Task)

Cross-model output speed comparison (Output Tokens / sec)

Conclusions

• Prominent speed and throughput advantages: While sustaining a 47-point intelligence level, Qwen3.7-Max delivers an output rate of 206.4 tok/s—3.5x faster than Claude Opus 5 (59 tok/s) and 5.3x faster than Kimi K3 (39 tok/s) —making it exceptionally well-suited for interactive coding Agents.

• Competitive cost-effectiveness: At $0.54 per task, its cost is only 23% of Claude Opus 5 ($2.34) , offering a clear cost advantage in enterprise IT and code refactoring scenarios requiring frequent reasoning calls.

• Elevated reasoning verbosity: Generating 100M tokens during the evaluation is noticeably higher than the industry median of 72M, indicating that the model tends to expand into very long chains of thought (Long-thought) during complex logical deduction, requiring appropriate max_tokens budget management.

Limitations

• Text-only input limitation: The model does not support visual or screenshot inputs (Multimodal Image Drop) , making it unusable for direct frontend UI screenshot reviews.

• Deprecation and migration: Following the release of Qwen3.8-Max (56 points) , the vendor has categorized Qwen3.7-Max under Deprecated archive-tracking status; long-term production deployments should evaluate a smooth migration path to version 3.8.

Reproduction steps

1. Call the qwen3.7-max-2026-05-20 snapshot via the official Alibaba Cloud Model Studio endpoint with enable_thinking: true enabled.

2. Run standard 10k input token baseline requests, recording TTFT (expected within the 2.0s–2.5s range) and streaming output tok/s.

3. Profile the proportion of thinking tokens in long-horizon reasoning tasks (Thinking vs. Final Answer) and calculate the actual completion cost per task.

Source excerpt or observation (brief excerpt for compliance only)

Artificial Analysis benchmark summary: “Qwen3.7 Max is amongst the leading models in intelligence... It's also notably fast, however somewhat verbose. At 206 tokens per second, Qwen3.7 Max is notably fast”.