DeepSeek-V4.1-Flash vs Qwen3-235B-A22B-Instruct-2507
DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 24.2. Qwen3-235B-A22B-Instruct-2507 is 1.6x 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 24.2, ranking #12 overall.
In the 1 individual benchmarks reported for both models, DeepSeek-V4.1-Flash wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, Qwen3-235B-A22B-Instruct-2507 is roughly 1.6x 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 #12 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- you process long inputs — it offers a 1,040,000 token context window
- you want the most recent training data — it shipped Sep 2026
Choose Qwen3-235B-A22B-Instruct-2507
- cost matters — it's about 1.6x cheaper per token
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 · 25 for Qwen3-235B-A22B-Instruct-2507
DeepSeek-V4.1-Flash outperforms in 1 benchmarks (GPQA), while Qwen3-235B-A22B-Instruct-2507 is better at 0 benchmarks.
DeepSeek-V4.1-Flash significantly outperforms across most 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 2.4x more expensive than Qwen3-235B-A22B-Instruct-2507 ($0.09/1M tokens).
For output processing, DeepSeek-V4.1-Flash ($0.66/1M tokens) is 1.2x more expensive than Qwen3-235B-A22B-Instruct-2507 ($0.55/1M tokens).
In conclusion, DeepSeek-V4.1-Flash is more expensive than Qwen3-235B-A22B-Instruct-2507.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4.1-Flash has 528.2B more parameters than Qwen3-235B-A22B-Instruct-2507, making it 224.8% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4.1-Flash accepts 1,040,000 input tokens compared to Qwen3-235B-A22B-Instruct-2507's 262,144 tokens. DeepSeek-V4.1-Flash can generate longer responses up to 393,216 tokens, while Qwen3-235B-A22B-Instruct-2507 is limited to 262,144 tokens.
Input capabilities
Documented input modalities across available providers
DeepSeek-V4.1-Flash supports multimodal inputs, whereas Qwen3-235B-A22B-Instruct-2507 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
Qwen3-235B-A22B-Instruct-2507
License
Usage and distribution terms
DeepSeek-V4.1-Flash is licensed under MIT, while Qwen3-235B-A22B-Instruct-2507 uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
DeepSeek-V4.1-Flash was released on 2026-09-10, while Qwen3-235B-A22B-Instruct-2507 was released on 2025-07-22.
DeepSeek-V4.1-Flash is 14 months newer than Qwen3-235B-A22B-Instruct-2507.
Sep 10, 2026
2 days ago
1.1yr newerJul 22, 2025
1.1 years 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. Qwen3-235B-A22B-Instruct-2507 is available from DeepInfra, Fireworks, Novita.
DeepSeek-V4.1-Flash
Qwen3-235B-A22B-Instruct-2507
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash and Qwen3-235B-A22B-Instruct-2507 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash vs Qwen3-235B-A22B-Instruct-2507.