DeepSeek-V4.1-Flash vs Qwen3.7 Max
DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 45.5. DeepSeek-V4.1-Flash is 5.7x cheaper per token.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to 45.5, ranking #13 overall.
The models split the 4 individual benchmarks reported for both models evenly.
On price, DeepSeek-V4.1-Flash is roughly 5.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4.1-Flash also accepts a larger context window (1,040,000 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V4.1-Flash
- overall performance matters — it scores 51.8 and ranks #13 on LLM Stats
- your work emphasizes coding and agents — it leads those capability indexes
- cost matters — it's about 5.7x cheaper per token
- you process long inputs — it offers a 1,040,000 token context window
- you want the most recent training data — it shipped Sep 2026
- you need open weights you can self-host or fine-tune
Choose Qwen3.7 Max
- you want predictable pricing at $1.25/M input and $3.75/M output
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
20 reported for DeepSeek-V4.1-Flash · 42 for Qwen3.7 Max
DeepSeek-V4.1-Flash outperforms in 2 benchmarks (MathArena Apex, NL2Repo), while Qwen3.7 Max is better at 2 benchmarks (GPQA, Humanity's Last Exam).
Both models are evenly matched across the benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4.1-Flash ($0.22/1M tokens) is 5.7x cheaper than Qwen3.7 Max ($1.25/1M tokens).
For output processing, DeepSeek-V4.1-Flash ($0.66/1M tokens) is 5.7x cheaper than Qwen3.7 Max ($3.75/1M tokens).
In conclusion, Qwen3.7 Max is more expensive than DeepSeek-V4.1-Flash.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
DeepSeek-V4.1-Flash accepts 1,040,000 input tokens compared to Qwen3.7 Max's 1,000,000 tokens. DeepSeek-V4.1-Flash can generate longer responses up to 393,216 tokens, while Qwen3.7 Max is limited to 65,536 tokens.
Input capabilities
Documented input modalities across available providers
DeepSeek-V4.1-Flash supports multimodal inputs, whereas Qwen3.7 Max does not.
DeepSeek-V4.1-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4.1-Flash
Qwen3.7 Max
License
Usage and distribution terms
DeepSeek-V4.1-Flash is licensed under MIT, while Qwen3.7 Max uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
DeepSeek-V4.1-Flash was released on 2026-09-10, while Qwen3.7 Max was released on 2026-05-19.
DeepSeek-V4.1-Flash is 4 months newer than Qwen3.7 Max.
Sep 10, 2026
5 days ago
3mo newerMay 19, 2026
3 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V4.1-Flash is available from Fireworks, DeepInfra, DeepSeek, Novita. Qwen3.7 Max is available from Novita, DeepInfra, Together.
DeepSeek-V4.1-Flash
Qwen3.7 Max
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash and Qwen3.7 Max side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash vs Qwen3.7 Max.