Gemini 2.5 Pro vs Llama-3.2-3B vs Pixtral 12B
Pixtral 12B comes out ahead, 64 to 54 and 49 on our weighted score, and it is the cheaper option too.
GoogleGemini 2.5 Pro
54/100- ECI145.3
- Price$1.25 / $10.00
- Context1.05M
MetaLlama-3.2-3B
49/100- ECI—
- Price$0.10 / $0.335
- Context131K
- Our pick
Mistral AIPixtral 12B
64/100- ECI—
- Price$0.15 / $0.15
- Context128K
Pixtral 12B is our pick
Pixtral 12B is the better all-round choice, scoring 64/100 against Gemini 2.5 Pro (54) and Llama-3.2-3B (49). Gemini 2.5 Pro wins on inputs & features and context window. 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 pricePixtral 12BPixtral 12B $0.15 · Llama-3.2-3B $0.159 · Gemini 2.5 Pro $3.44 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 ProGemini 2.5 Pro 1,048,576 · Llama-3.2-3B 131,072 · Pixtral 12B 128,000 tokens
- Widest inputsGemini 2.5 ProGemini 2.5 Pro: Text, Images, PDFs, Audio, Video · Llama-3.2-3B: Text · Pixtral 12B: Text, Images
- Self-hostingLlama-3.2-3B and Pixtral 12BPublishes downloadable weights (Llama 3.2 Community License)
| Measure | Weight | Gemini 2.5 Pro | Llama-3.2-3B | Pixtral 12B |
|---|---|---|---|---|
| Price | 50% | 24 | 88 | 89 |
| Inputs & features | 30% | 100 | 0 | 50 |
| Context window | 20% | 61 | 24 | 24 |
| Overall | 100% | 54/100 | 49/100 | 64/100 |
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 ProGoogle | Llama-3.2-3BMeta | Pixtral 12BMistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.3 | — | — |
| ECI rank | #78 of 148 | — | — |
| GPQA DiamondGraduate-level science questions | 85.3% | — | — |
| FrontierMath Tiers 1–3Research-level mathematics | 24.6% | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 84.7% | — | — |
| SWE-bench VerifiedFixing real GitHub issues | 57.6% | — | — |
| Price per million tokens | |||
| Input | $1.25 | $0.10 (best) | $0.15 |
| Output | $10.00 | $0.335 | $0.15 (best) |
| Cached input | $0.125 | — | — |
| Blended (3:1) | $3.44 | $0.159 | $0.15 (best) |
| Long-context rate | Over 200K: $2.50 / $15.00 | Same rate | Same rate |
| Price source | Official Google API | Median of 3 providers | Official Mistral API |
| Limits | |||
| Context window | 1,048,576 tokens (best) | 131,072 tokens | 128,000 tokens |
| Max output | 65,536 tokens | 8,192 tokens | 128,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | Yes | No | No |
| Audio | Yes | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | No | No |
| Tool calling | Yes | No | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | OpenLlama 3.2 Community License | Open |
| API model ID | gemini-2.5-pro | — | pixtral-12b |
| API providers | 22 (best) | 3 | 4 |
| Released | Jun 17, 2025 | Sep 25, 2024 | Sep 1, 2024 |
| Knowledge cutoff | Jan 2025 | Dec 2023 | Sep 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 Pro$32.50
- Llama-3.2-3B$1.67
- Pixtral 12B$1.80
Which should you choose?
Which is better: Gemini 2.5 Pro, Llama-3.2-3B or Pixtral 12B?
Pixtral 12B is the better all-round choice, scoring 64/100 against Gemini 2.5 Pro (54) and Llama-3.2-3B (49). Gemini 2.5 Pro wins on inputs & features and context window. 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 Pro, Llama-3.2-3B or Pixtral 12B?
Pixtral 12B is cheaper at $0.15 input / $0.15 output per million tokens (official Mistral API price). Llama-3.2-3B costs $0.10 input / $0.335 output per million tokens (median across 3 API providers); Gemini 2.5 Pro costs $1.25 input / $10.00 output per million tokens (official Google API price). At a typical mix of three input tokens to one output token, that is $0.15 per million tokens for Pixtral 12B versus $0.159 for Llama-3.2-3B (1.1× as much) and $3.44 for Gemini 2.5 Pro (23× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Gemini 2.5 Pro has an ECI of 145.3, Llama-3.2-3B has not been scored yet and Pixtral 12B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Llama-3.2-3B and Pixtral 12B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Llama-3.2-3B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Gemini 2.5 Pro has the largest context window at 1,048,576 tokens, against 131,072 for Llama-3.2-3B and 128,000 for Pixtral 12B. Maximum output per response: Gemini 2.5 Pro up to 65,536, Llama-3.2-3B up to 8,192, Pixtral 12B up to 128,000 tokens.
Which can read images, PDFs, audio or video?
Gemini 2.5 Pro accepts text, images, PDFs, audio and video; Llama-3.2-3B accepts text; Pixtral 12B accepts text and images. Gemini 2.5 Pro handles the widest range of inputs.
Are any of these open source?
Llama-3.2-3B and Pixtral 12B publishes its weights (Llama 3.2 Community License) and can be self-hosted; Gemini 2.5 Pro is proprietary.
Which is newer?
Gemini 2.5 Pro is the newest, released Jun 17, 2025. Llama-3.2-3B came out Sep 25, 2024; Pixtral 12B came out Sep 1, 2024. Knowledge cutoff: Gemini 2.5 Pro Jan 2025, Llama-3.2-3B Dec 2023, Pixtral 12B Sep 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.
