Qwen3 235B-A22B vs Mistral Small 3.2 vs DeepSeek-R1
Mistral Small 3.2 comes out ahead, 60 to 51 and 51 on our weighted score, and it is the cheaper option too.
Alibaba (Qwen)Qwen3 235B-A22B
51/100- ECI139.4
- Price$0.70 / $2.80
- Context131K
- Our pick
Mistral AIMistral Small 3.2
60/100- ECI131.7
- Price$0.10 / $0.30
- Context128K
DeepSeekDeepSeek-R1
51/100- ECI139.0
- Price$0.70 / $2.60
- Context128K
Mistral Small 3.2 is our pick
Mistral Small 3.2 is the better all-round choice, scoring 60/100 against Qwen3 235B-A22B (51) and DeepSeek-R1 (51). It leads on price and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen3 235B-A22BCapabilities Index (ECI): Qwen3 235B-A22B 139.4 · DeepSeek-R1 139.0 · Mistral Small 3.2 131.7
- Lowest priceMistral Small 3.2Mistral Small 3.2 $0.15 · DeepSeek-R1 $1.18 · Qwen3 235B-A22B $1.23 per 1M tokens (3:1 blend)
- Longest contextQwen3 235B-A22BQwen3 235B-A22B 131,072 · Mistral Small 3.2 128,000 · DeepSeek-R1 128,000 tokens
- Widest inputsMistral Small 3.2Qwen3 235B-A22B: Text · Mistral Small 3.2: Text, Images · DeepSeek-R1: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen3 235B-A22B | Mistral Small 3.2 | DeepSeek-R1 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 65 | 55 | 64 |
| Price | 25% | 46 | 89 | 47 |
| Inputs & features | 15% | 35 | 50 | 35 |
| Context window | 10% | 24 | 24 | 24 |
| Overall | 100% | 51/100 | 60/100 | 51/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Qwen3 235B-A22BAlibaba (Qwen) | Mistral Small 3.2Mistral AI | DeepSeek-R1DeepSeek |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 139.4 (best) | 131.7 | 139.0 |
| ECI rank | #103 of 148 (best) | #123 of 148 | #104 of 148 |
| GPQA DiamondGraduate-level science questions | 70.7% | 49.1% | 71.7% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 30.3% | 53.3% (best) |
| Price per million tokens | |||
| Input | $0.70 | $0.10 (best) | $0.70 |
| Output | $2.80 | $0.30 (best) | $2.60 |
| Cached input | — | — | — |
| Blended (3:1) | $1.23 | $0.15 (best) | $1.18 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Official Mistral API | Median of 11 providers |
| Limits | |||
| Context window | 131,072 tokens (best) | 128,000 tokens | 128,000 tokens |
| Max output | 16,384 tokens | 16,384 tokens | 32,768 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | qwen3-235b-a22b | mistral-small-2506 | — |
| API providers | 7 | 6 | 12 (best) |
| Released | Apr 28, 2025 | Jun 20, 2025 | Jan 20, 2025 |
| Knowledge cutoff | Apr 2025 | Mar 2025 | Jul 2024 |
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$12.60
- Mistral Small 3.2$1.60
- DeepSeek-R1$12.20
Which should you choose?
Which is better: Qwen3 235B-A22B, Mistral Small 3.2 or DeepSeek-R1?
Mistral Small 3.2 is the better all-round choice, scoring 60/100 against Qwen3 235B-A22B (51) and DeepSeek-R1 (51). It leads on price and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Qwen3 235B-A22B, Mistral Small 3.2 or DeepSeek-R1?
Mistral Small 3.2 is cheaper at $0.10 input / $0.30 output per million tokens (official Mistral API price). DeepSeek-R1 costs $0.70 input / $2.60 output per million tokens (median across 11 API providers); Qwen3 235B-A22B costs $0.70 input / $2.80 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.15 per million tokens for Mistral Small 3.2 versus $1.18 for DeepSeek-R1 (7.8× as much) and $1.23 for Qwen3 235B-A22B (8.2× as much).
Which scores higher on benchmarks?
Qwen3 235B-A22B scores higher on the Capabilities Index (ECI): Qwen3 235B-A22B 139.4 (#103 of 148), DeepSeek-R1 139.0 (#104 of 148) and Mistral Small 3.2 131.7 (#123 of 148). The confidence ranges of the top two overlap (135.2–140.8 vs 136.2–140.4), so treat the gap as small. On individual benchmarks: GPQA Diamond — DeepSeek-R1 71.7%, Qwen3 235B-A22B 70.7%, Mistral Small 3.2 49.1%.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3 235B-A22B, Mistral Small 3.2 and DeepSeek-R1 yet, so there is no like-for-like coding score. On overall capability, Qwen3 235B-A22B leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
Qwen3 235B-A22B has the largest context window at 131,072 tokens, against 128,000 for Mistral Small 3.2 and 128,000 for DeepSeek-R1. Maximum output per response: Qwen3 235B-A22B up to 16,384, Mistral Small 3.2 up to 16,384, DeepSeek-R1 up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Qwen3 235B-A22B accepts text; Mistral Small 3.2 accepts text and images; DeepSeek-R1 accepts text. Mistral Small 3.2 handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Mistral Small 3.2 is the newest, released Jun 20, 2025. Qwen3 235B-A22B came out Apr 28, 2025; DeepSeek-R1 came out Jan 20, 2025. Knowledge cutoff: Qwen3 235B-A22B Apr 2025, Mistral Small 3.2 Mar 2025, DeepSeek-R1 Jul 2024.
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 Oct 4, 2026.
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