DeepSeek V4.1 Flash 1017 vs Qwen3 235B-A22B Thinking 2507
DeepSeek V4.1 Flash 1017 comes out ahead, 79 to 55 on our weighted score, and it is the cheaper option too.
- Our pick
DeepSeekDeepSeek V4.1 Flash 1017
79/100- ECI158.8
- Price$0.15 / $0.60
- Context1M
Alibaba (Qwen)Qwen3 235B-A22B Thinking 2507
55/100- ECI143.8
- Price$0.30 / $2.90
- Context262K
Add a model
Make it a three-way comparison.
DeepSeek V4.1 Flash 1017 is our pick
DeepSeek V4.1 Flash 1017 is the better all-round choice, scoring 79/100 against Qwen3 235B-A22B Thinking 2507 (55). It leads on capability, price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityDeepSeek V4.1 Flash 1017Capabilities Index (ECI): DeepSeek V4.1 Flash 1017 158.8 · Qwen3 235B-A22B Thinking 2507 143.8
- Lowest priceDeepSeek V4.1 Flash 1017DeepSeek V4.1 Flash 1017 $0.263 · Qwen3 235B-A22B Thinking 2507 $0.95 per 1M tokens (3:1 blend)
- Longest contextDeepSeek V4.1 Flash 1017DeepSeek V4.1 Flash 1017 1,000,000 · Qwen3 235B-A22B Thinking 2507 262,144 tokens
- Widest inputsDeepSeek V4.1 Flash 1017DeepSeek V4.1 Flash 1017: Text, Images · Qwen3 235B-A22B Thinking 2507: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | DeepSeek V4.1 Flash 1017 | Qwen3 235B-A22B Thinking 2507 |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 87 | 69 |
| Price | 25% | 74 | 49 |
| Inputs & features | 15% | 70 | 35 |
| Context window | 10% | 60 | 37 |
| Overall | 100% | 79/100 | 55/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | DeepSeek V4.1 Flash 1017DeepSeek | Qwen3 235B-A22B Thinking 2507Alibaba (Qwen) |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 158.8 (best) | 143.8 |
| ECI rank | #19 of 165 (best) | #93 of 165 |
| GPQA DiamondGraduate-level science questions | 93.2% (best) | 80.1% |
| FrontierMath Tiers 1–3Research-level mathematics | 71.2% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 99.4% (best) | 86.7% |
| SimpleQA VerifiedShort factual questions | 42.5% (best) | 40.4% |
| Price per million tokens | ||
| Input | $0.15 (best) | $0.30 |
| Output | $0.60 (best) | $2.90 |
| Cached input | $0.003 | — |
| Blended (3:1) | $0.263 (best) | $0.95 |
| Long-context rate | Same rate | Same rate |
| Price source | Official DeepSeek API | Median of 11 providers |
| Limits | ||
| Context window | 1,000,000 tokens (best) | 262,144 tokens |
| Max output | 384,000 tokens (best) | 81,920 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yeslow · high · max | Yes |
| Tool calling | Yes | Yes |
| Structured output | Yes | No |
| Availability | ||
| Weights | OpenMIT | OpenApache 2.0 |
| API model ID | deepseek-flash | — |
| API providers | 40 (best) | 12 |
| Released | Oct 17, 2026 | Jul 25, 2025 |
| Knowledge cutoff | May 2025 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- DeepSeek V4.1 Flash 1017$2.70
- Qwen3 235B-A22B Thinking 2507$8.80
Which should you choose?
Which is better: DeepSeek V4.1 Flash 1017 or Qwen3 235B-A22B Thinking 2507?
DeepSeek V4.1 Flash 1017 is the better all-round choice, scoring 79/100 against Qwen3 235B-A22B Thinking 2507 (55). It leads on capability, price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, DeepSeek V4.1 Flash 1017 or Qwen3 235B-A22B Thinking 2507?
DeepSeek V4.1 Flash 1017 is cheaper at $0.15 input / $0.60 output per million tokens (official DeepSeek API price). Qwen3 235B-A22B Thinking 2507 costs $0.30 input / $2.90 output per million tokens (median across 11 API providers). At a typical mix of three input tokens to one output token, that is $0.263 per million tokens for DeepSeek V4.1 Flash 1017 versus $0.95 for Qwen3 235B-A22B Thinking 2507 (3.6× as much).
Which scores higher on benchmarks?
DeepSeek V4.1 Flash 1017 scores higher on the Capabilities Index (ECI): DeepSeek V4.1 Flash 1017 158.8 (#19 of 165) and Qwen3 235B-A22B Thinking 2507 143.8 (#93 of 165). Their confidence ranges do not overlap (156.7–162.2 vs 141.9–145.2), so the gap is a real one. On individual benchmarks: GPQA Diamond — DeepSeek V4.1 Flash 1017 93.2%, Qwen3 235B-A22B Thinking 2507 80.1%; OTIS Mock AIME 2024–2025 — DeepSeek V4.1 Flash 1017 99.4%, Qwen3 235B-A22B Thinking 2507 86.7%; SimpleQA Verified — DeepSeek V4.1 Flash 1017 42.5%, Qwen3 235B-A22B Thinking 2507 40.4%.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek V4.1 Flash 1017 and Qwen3 235B-A22B Thinking 2507 yet, so there is no like-for-like coding score. On overall capability, DeepSeek V4.1 Flash 1017 leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.
Which has the bigger context window?
DeepSeek V4.1 Flash 1017 has the largest context window at 1,000,000 tokens, against 262,144 for Qwen3 235B-A22B Thinking 2507. Maximum output per response: DeepSeek V4.1 Flash 1017 up to 384,000, Qwen3 235B-A22B Thinking 2507 up to 81,920 tokens.
Which can read images, PDFs, audio or video?
DeepSeek V4.1 Flash 1017 accepts text and images; Qwen3 235B-A22B Thinking 2507 accepts text. DeepSeek V4.1 Flash 1017 handles the widest range of inputs.
Are any of these open source?
Yes, both publish their weights (MIT and Apache 2.0), so you can self-host them.
Which is newer?
DeepSeek V4.1 Flash 1017 is the newest, released Oct 17, 2026. Qwen3 235B-A22B Thinking 2507 came out Jul 25, 2025. Knowledge cutoff: DeepSeek V4.1 Flash 1017 May 2025.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Nov 2, 2026.
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