Gemini 2.0 Flash-Lite vs Mistral Small 3.2 vs Phi-4-mini
Gemini 2.0 Flash-Lite comes out ahead, 78 to 40 and 25 on our weighted score.
- Our pick
GoogleGemini 2.0 Flash-Lite
78/100- ECI—
- Price—
- Context1.05M
Mistral AIMistral Small 3.2
40/100- ECI131.7
- Price$0.10 / $0.30
- Context128K
MicrosoftPhi-4-mini
25/100- ECI—
- Price$0.075 / $0.30
- Context128K
Gemini 2.0 Flash-Lite is our pick
Gemini 2.0 Flash-Lite is the better all-round choice, scoring 78/100 against Mistral Small 3.2 (40) and Phi-4-mini (25). It leads on inputs & features and context window. The score weighs inputs & features 60%, context window 40%. 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 pricePhi-4-miniPhi-4-mini $0.131 · Mistral Small 3.2 $0.15 per 1M tokens (3:1 blend) · Gemini 2.0 Flash-Lite unpriced
- Longest contextGemini 2.0 Flash-LiteGemini 2.0 Flash-Lite 1,048,576 · Mistral Small 3.2 128,000 · Phi-4-mini 128,000 tokens
- Widest inputsGemini 2.0 Flash-LiteGemini 2.0 Flash-Lite: Text, Images, PDFs, Audio, Video · Mistral Small 3.2: Text, Images · Phi-4-mini: Text
- Self-hostingMistral Small 3.2 and Phi-4-miniPublishes downloadable weights
| Measure | Weight | Gemini 2.0 Flash-Lite | Mistral Small 3.2 | Phi-4-mini |
|---|---|---|---|---|
| Inputs & features | 60% | 90 | 50 | 25 |
| Context window | 40% | 61 | 24 | 24 |
| Overall | 100% | 78/100 | 40/100 | 25/100 |
Left out because at least one model lacks the data: capability and price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Gemini 2.0 Flash-LiteGoogle | Mistral Small 3.2Mistral AI | Phi-4-miniMicrosoft |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 131.7 | — |
| ECI rank | — | #123 of 148 | — |
| GPQA DiamondGraduate-level science questions | — | 49.1% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 30.3% | — |
| Price per million tokens | |||
| Input | — | $0.10 | $0.075 (best) |
| Output | — | $0.30 | $0.30 |
| Cached input | — | — | — |
| Blended (3:1) | — | $0.15 | $0.131 (best) |
| Long-context rate | — | Same rate | Same rate |
| Price source | — | Official Mistral API | Official Azure API |
| Limits | |||
| Context window | 1,048,576 tokens (best) | 128,000 tokens | 128,000 tokens |
| Max output | 8,192 tokens | 16,384 tokens (best) | 4,096 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 | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | — | mistral-small-2506 | phi-4-mini |
| API providers | — | 6 (best) | 1 |
| Released | Dec 11, 2024 | Jun 20, 2025 | Dec 11, 2024 |
| Knowledge cutoff | Jun 2024 | Mar 2025 | Oct 2023 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Gemini 2.0 Flash-Lite—
- Mistral Small 3.2$1.60
- Phi-4-mini$1.35
Which should you choose?
Which is better: Gemini 2.0 Flash-Lite, Mistral Small 3.2 or Phi-4-mini?
Gemini 2.0 Flash-Lite is the better all-round choice, scoring 78/100 against Mistral Small 3.2 (40) and Phi-4-mini (25). It leads on inputs & features and context window. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Gemini 2.0 Flash-Lite, Mistral Small 3.2 or Phi-4-mini?
Phi-4-mini is cheaper at $0.075 input / $0.30 output per million tokens (official Azure API price). Mistral Small 3.2 costs $0.10 input / $0.30 output per million tokens (official Mistral API price). At a typical mix of three input tokens to one output token, that is $0.131 per million tokens for Phi-4-mini versus $0.15 for Mistral Small 3.2 (1.1× as much). Gemini 2.0 Flash-Lite has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Gemini 2.0 Flash-Lite has not been scored yet, Mistral Small 3.2 has an ECI of 131.7 and Phi-4-mini has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Gemini 2.0 Flash-Lite, Mistral Small 3.2 and Phi-4-mini 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 2.0 Flash-Lite has the largest context window at 1,048,576 tokens, against 128,000 for Mistral Small 3.2 and 128,000 for Phi-4-mini. Maximum output per response: Gemini 2.0 Flash-Lite up to 8,192, Mistral Small 3.2 up to 16,384, Phi-4-mini up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Gemini 2.0 Flash-Lite accepts text, images, PDFs, audio and video; Mistral Small 3.2 accepts text and images; Phi-4-mini accepts text. Gemini 2.0 Flash-Lite handles the widest range of inputs.
Are any of these open source?
Mistral Small 3.2 and Phi-4-mini publishes its weights and can be self-hosted; Gemini 2.0 Flash-Lite is proprietary.
Which is newer?
Mistral Small 3.2 is the newest, released Jun 20, 2025. Gemini 2.0 Flash-Lite came out Dec 11, 2024; Phi-4-mini came out Dec 11, 2024. Knowledge cutoff: Gemini 2.0 Flash-Lite Jun 2024, Mistral Small 3.2 Mar 2025, Phi-4-mini Oct 2023.
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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