Gemini 2.5 Flash-Lite vs Mistral Small 3.2 vs Qwen Turbo
Gemini 2.5 Flash-Lite comes out ahead, 85 to 72 and 64 on our weighted score, though Qwen Turbo is 2× cheaper per token.
- Our pick
GoogleGemini 2.5 Flash-Lite
85/100- ECI133.9
- Price$0.10 / $0.40
- Context1.05M
Mistral AIMistral Small 3.2
64/100- ECI131.7
- Price$0.10 / $0.30
- Context128K
Alibaba (Qwen)Qwen Turbo
72/100- ECI—
- Price$0.05 / $0.20
- Context1M
Gemini 2.5 Flash-Lite is our pick
Gemini 2.5 Flash-Lite is the better all-round choice, scoring 85/100 against Qwen Turbo (72) and Mistral Small 3.2 (64). It leads on inputs & features. Qwen Turbo wins on price. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceQwen TurboQwen Turbo $0.087 · Mistral Small 3.2 $0.15 · Gemini 2.5 Flash-Lite $0.175 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 Flash-LiteGemini 2.5 Flash-Lite 1,048,576 · Qwen Turbo 1,000,000 · Mistral Small 3.2 128,000 tokens
- Widest inputsGemini 2.5 Flash-LiteGemini 2.5 Flash-Lite: Text, Images, PDFs, Audio, Video · Mistral Small 3.2: Text, Images · Qwen Turbo: Text
- Self-hostingMistral Small 3.2Publishes downloadable weights
| Measure | Weight | Gemini 2.5 Flash-Lite | Mistral Small 3.2 | Qwen Turbo |
|---|---|---|---|---|
| Price | 50% | 86 | 89 | 100 |
| Inputs & features | 30% | 100 | 50 | 35 |
| Context window | 20% | 61 | 24 | 60 |
| Overall | 100% | 85/100 | 64/100 | 72/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Gemini 2.5 Flash-LiteGoogle | Mistral Small 3.2Mistral AI | Qwen TurboAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 133.9 (best) | 131.7 | — |
| ECI rank | #118 of 148 (best) | #123 of 148 | — |
| GPQA DiamondGraduate-level science questions | — | 49.1% (best) | 41.8% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 30.3% (best) | 6.1% |
| Price per million tokens | |||
| Input | $0.10 | $0.10 | $0.05 (best) |
| Output | $0.40 | $0.30 | $0.20 (best) |
| Cached input | $0.01 | — | — |
| Blended (3:1) | $0.175 | $0.15 | $0.087 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Google API | Official Mistral API | Official Alibaba API |
| Limits | |||
| Context window | 1,048,576 tokens (best) | 128,000 tokens | 1,000,000 tokens |
| Max output | 65,536 tokens (best) | 16,384 tokens | 16,384 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | Yes | No | No |
| Audio | Yes | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | gemini-2.5-flash-lite | mistral-small-2506 | qwen-turbo |
| API providers | 20 (best) | 6 | 3 |
| Released | Jun 17, 2025 | Jun 20, 2025 | Nov 1, 2024 |
| Knowledge cutoff | Jan 2025 | Mar 2025 | Apr 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Gemini 2.5 Flash-Lite$1.80
- Mistral Small 3.2$1.60
- Qwen Turbo$0.90
Which should you choose?
Which is better: Gemini 2.5 Flash-Lite, Mistral Small 3.2 or Qwen Turbo?
Gemini 2.5 Flash-Lite is the better all-round choice, scoring 85/100 against Qwen Turbo (72) and Mistral Small 3.2 (64). It leads on inputs & features. Qwen Turbo wins on price. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Gemini 2.5 Flash-Lite, Mistral Small 3.2 or Qwen Turbo?
Qwen Turbo is cheaper at $0.05 input / $0.20 output per million tokens (official Alibaba API price). Mistral Small 3.2 costs $0.10 input / $0.30 output per million tokens (official Mistral API price); Gemini 2.5 Flash-Lite costs $0.10 input / $0.40 output per million tokens (official Google API price). At a typical mix of three input tokens to one output token, that is $0.087 per million tokens for Qwen Turbo versus $0.15 for Mistral Small 3.2 (1.7× as much) and $0.175 for Gemini 2.5 Flash-Lite (2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Gemini 2.5 Flash-Lite has an ECI of 133.9, Mistral Small 3.2 has an ECI of 131.7 and Qwen Turbo has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Gemini 2.5 Flash-Lite, Mistral Small 3.2 and Qwen Turbo yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
Gemini 2.5 Flash-Lite has the largest context window at 1,048,576 tokens, against 1,000,000 for Qwen Turbo and 128,000 for Mistral Small 3.2. Maximum output per response: Gemini 2.5 Flash-Lite up to 65,536, Mistral Small 3.2 up to 16,384, Qwen Turbo up to 16,384 tokens.
Which can read images, PDFs, audio or video?
Gemini 2.5 Flash-Lite accepts text, images, PDFs, audio and video; Mistral Small 3.2 accepts text and images; Qwen Turbo accepts text. Gemini 2.5 Flash-Lite handles the widest range of inputs.
Are any of these open source?
Mistral Small 3.2 publishes its weights and can be self-hosted; Gemini 2.5 Flash-Lite and Qwen Turbo is proprietary.
Which is newer?
Mistral Small 3.2 is the newest, released Jun 20, 2025. Gemini 2.5 Flash-Lite came out Jun 17, 2025; Qwen Turbo came out Nov 1, 2024. Knowledge cutoff: Gemini 2.5 Flash-Lite Jan 2025, Mistral Small 3.2 Mar 2025, Qwen Turbo Apr 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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