Qwen3 235B-A22B Thinking 2507 vs DeepSeek R1 0528
Too close to call on our weighted score (Qwen3 235B-A22B Thinking 2507 55, DeepSeek R1 0528 53). The right pick depends on what you value most.
Alibaba (Qwen)
Qwen3 235B-A22B Thinking 2507
55/100- ECI143.8
- Price$0.30 / $2.90
- Context262K
DeepSeek
DeepSeek R1 0528
53/100- ECI141.2
- Price$0.512 / $2.19
- Context164K
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Make it a three-way comparison.
Too close to call
It is close. Our weighted score puts them within 2 points (Qwen3 235B-A22B Thinking 2507 55/100, DeepSeek R1 0528 53/100), so choose by what matters most for your work: Qwen3 235B-A22B Thinking 2507 for raw capability and DeepSeek R1 0528 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen3 235B-A22B Thinking 2507Capabilities Index (ECI): Qwen3 235B-A22B Thinking 2507 143.8 · DeepSeek R1 0528 141.2
- Lowest priceDeepSeek R1 0528DeepSeek R1 0528 $0.932 · Qwen3 235B-A22B Thinking 2507 $0.95 per 1M tokens (3:1 blend)
- Longest contextQwen3 235B-A22B Thinking 2507Qwen3 235B-A22B Thinking 2507 262,144 · DeepSeek R1 0528 163,840 tokens
- Widest inputsSame inputsQwen3 235B-A22B Thinking 2507: Text · DeepSeek R1 0528: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen3 235B-A22B Thinking 2507 | DeepSeek R1 0528 |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 69 | 66 |
| Price | 25% | 49 | 49 |
| Inputs & features | 15% | 35 | 35 |
| Context window | 10% | 37 | 28 |
| Overall | 100% | 55/100 | 53/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | ||
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 143.8 (best) | 141.2 |
| ECI rank | #93 of 165 (best) | #108 of 165 |
| GPQA DiamondGraduate-level science questions | 80.1% (best) | 76.3% |
| OTIS Mock AIME 2024–2025Competition mathematics | 86.7% (best) | 66.4% |
| SimpleQA VerifiedShort factual questions | 40.4% | — |
| Price per million tokens | ||
| Input | $0.30 (best) | $0.512 |
| Output | $2.90 | $2.19 (best) |
| Cached input | — | — |
| Blended (3:1) | $0.95 | $0.932 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 11 providers | Median of 16 providers |
| Limits | ||
| Context window | 262,144 tokens (best) | 163,840 tokens |
| Max output | 81,920 tokens (best) | 32,768 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes | Yes |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | OpenApache 2.0 | OpenMIT |
| API model ID | — | — |
| API providers | 12 | 16 (best) |
| Released | Jul 25, 2025 | May 28, 2025 |
| Knowledge cutoff | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
Qwen3 235B-A22B Thinking 2507$8.80
DeepSeek R1 0528$9.50
Which should you choose?
Which is better: Qwen3 235B-A22B Thinking 2507 or DeepSeek R1 0528?
It is close. Our weighted score puts them within 2 points (Qwen3 235B-A22B Thinking 2507 55/100, DeepSeek R1 0528 53/100), so choose by what matters most for your work: Qwen3 235B-A22B Thinking 2507 for raw capability and DeepSeek R1 0528 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Qwen3 235B-A22B Thinking 2507 or DeepSeek R1 0528?
DeepSeek R1 0528 is cheaper at $0.512 input / $2.19 output per million tokens (median across 16 API providers). 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 $0.932 per million tokens for DeepSeek R1 0528 versus $0.95 for Qwen3 235B-A22B Thinking 2507 (1× as much).
Which scores higher on benchmarks?
Qwen3 235B-A22B Thinking 2507 scores higher on the Capabilities Index (ECI): Qwen3 235B-A22B Thinking 2507 143.8 (#93 of 165) and DeepSeek R1 0528 141.2 (#108 of 165). The confidence ranges of the top two overlap (141.9–145.2 vs 139.0–142.9), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen3 235B-A22B Thinking 2507 80.1%, DeepSeek R1 0528 76.3%; OTIS Mock AIME 2024–2025 — Qwen3 235B-A22B Thinking 2507 86.7%, DeepSeek R1 0528 66.4%.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3 235B-A22B Thinking 2507 and DeepSeek R1 0528 yet, so there is no like-for-like coding score. On overall capability, Qwen3 235B-A22B Thinking 2507 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?
Qwen3 235B-A22B Thinking 2507 has the largest context window at 262,144 tokens, against 163,840 for DeepSeek R1 0528. Maximum output per response: Qwen3 235B-A22B Thinking 2507 up to 81,920, DeepSeek R1 0528 up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Qwen3 235B-A22B Thinking 2507 accepts text; DeepSeek R1 0528 accepts text. They handle the same number of input types.
Are any of these open source?
Yes, both publish their weights (Apache 2.0 and MIT), so you can self-host them.
Which is newer?
Qwen3 235B-A22B Thinking 2507 is the newest, released Jul 25, 2025. DeepSeek R1 0528 came out May 28, 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.