Qwen3.7 Max vs Claude 4.1 Opus
Head-to-head specifications
| Metric | Qwen3.7 Max | Claude 4.1 Opus | Difference |
|---|---|---|---|
| Intelligence Index | 46.0 | 30.0 | +53.3% |
| Context window | 1M tokens | 262K tokens | — |
| Blended price ($/1M tokens) | $0.87 | $1.68 | -48.2% |
| Output speed (tokens/s) | 200 | 30 | +566.7% |
| Access | Open weights | Proprietary API | — |
- Qwen3.7 Max leads overall capability (Intelligence Index 46.0 vs 30.0).
- Qwen3.7 Max is the cheaper model to run at $0.87/1M blended tokens — about 1.9× cheaper.
- Qwen3.7 Max offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: Qwen3.7 Max or Claude 4.1 Opus?
Qwen3.7 Max advantages
- General intelligence (+35%)
- Context window (+74%)
- Affordability (+48%)
- Output speed (+85%)
Claude 4.1 Opus advantages
- No decisive advantage on the tracked metrics.
Which should you choose?
- Choose the Qwen3.7 Max if you need the strongest overall reasoning and accuracy.
- Choose the Qwen3.7 Max if you work with long documents or large codebases.
Value for money
Qwen3.7 Max offers more intelligence per dollar (3.0× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
Qwen3.7 Max vs Claude 4.1 Opus: which should you choose?
Qwen3.7 Max — Alibaba text model with an Intelligence Index of 46, a 1M-token context window and a blended price of $0.87/1M tokens (open weights).
Claude 4.1 Opus — Anthropic multimodal model with an Intelligence Index of 30, a 262K-token context window and a blended price of $1.68/1M tokens.
Qwen3.7 Max vs Claude 4.1 Opus: Qwen3.7 Max scores higher on the Intelligence Index. Qwen3.7 Max leads overall capability (Intelligence Index 46.0 vs 30.0). Qwen3.7 Max is the cheaper model to run at $0.87/1M blended tokens — about 1.9× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Qwen3.7 Max scores 46.0 versus 30.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Qwen3.7 Max accepts up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, Qwen3.7 Max generates faster (200 vs 30 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Qwen3.7 Max is the cheaper model to run ($0.87 vs $1.68 per 1M tokens). Qwen3.7 Max is open weights and Claude 4.1 Opus is proprietary api. Open-weight models can be self-hosted, trading per-call cost for infrastructure you manage; for production also weigh rate limits, throughput and data-residency requirements.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the Qwen3.7 Max better than the Claude 4.1 Opus?
Qwen3.7 Max is the clearly stronger overall choice, winning most of the dimensions that matter. Qwen3.7 Max leads overall capability (Intelligence Index 46.0 vs 30.0).
What is the main difference between the Qwen3.7 Max and the Claude 4.1 Opus?
Qwen3.7 Max leads overall capability (Intelligence Index 46.0 vs 30.0). Qwen3.7 Max is the cheaper model to run at $0.87/1M blended tokens — about 1.9× cheaper.
Which is better value?
Qwen3.7 Max offers more intelligence per dollar (3.0× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
Which should I choose?
Choose the Qwen3.7 Max if you need the strongest overall reasoning and accuracy. Choose the Qwen3.7 Max if you work with long documents or large codebases.
Methodology
Large language models are compared on independent leaderboard metrics: an Intelligence Index (a composite of reasoning and knowledge evaluations), Coding and Agentic indices where measured, community arena Elo, maximum context window, a blended API price per million tokens (weighted across cache-hit, input and output rates), and measured output speed in tokens per second. Where a model ships multiple reasoning-effort variants, we report its strongest variant. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit — and this space moves quickly, so figures reflect the leaderboard snapshot on the page date.