Codestral-22B-v0.1 vs Gemma 2 2B IT
Too close to call on our weighted score (Codestral-22B-v0.1 0, Gemma 2 2B IT 0). The right pick depends on what you value most.
Mistral AICodestral-22B-v0.1
0/100- ECI—
- Price—
- Context33K
GoogleGemma 2 2B IT
0/100- ECI—
- Price—
- Context8K
Add a model
Make it a three-way comparison.
Too close to call
It is close. Our weighted score puts them within a point (Codestral-22B-v0.1 0/100, Gemma 2 2B IT 0/100), so choose by what matters most for your work: Codestral-22B-v0.1 for long inputs. 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
- Longest contextCodestral-22B-v0.1Codestral-22B-v0.1 32,768 · Gemma 2 2B IT 8,192 tokens
- Widest inputsSame inputsCodestral-22B-v0.1: Text · Gemma 2 2B IT: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Codestral-22B-v0.1 | Gemma 2 2B IT |
|---|---|---|---|
| Inputs & features | 60% | 0 | 0 |
| Context window | 40% | 0 | 0 |
| Overall | 100% | 0/100 | 0/100 |
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 | Codestral-22B-v0.1Mistral AI | Gemma 2 2B ITGoogle |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | — | — |
| Output | — | — |
| Cached input | — | — |
| Blended (3:1) | — | — |
| Long-context rate | — | — |
| Price source | — | — |
| Limits | ||
| Context window | 32,768 tokens (best) | 8,192 tokens |
| Max output | 8,192 tokens | 8,192 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | No |
| Tool calling | No | No |
| Structured output | No | No |
| Availability | ||
| Weights | OpenMistral AI Non-Production License | OpenGemma Terms of Use |
| API model ID | — | — |
| API providers | — | — |
| Released | May 29, 2024 | Jul 31, 2024 |
| 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.
- Codestral-22B-v0.1—
- Gemma 2 2B IT—
Which should you choose?
Which is better: Codestral-22B-v0.1 or Gemma 2 2B IT?
It is close. Our weighted score puts them within a point (Codestral-22B-v0.1 0/100, Gemma 2 2B IT 0/100), so choose by what matters most for your work: Codestral-22B-v0.1 for long inputs. 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, Codestral-22B-v0.1 or Gemma 2 2B IT?
None of these models has a published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Codestral-22B-v0.1 has not been scored yet and Gemma 2 2B IT has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Codestral-22B-v0.1 and Gemma 2 2B IT yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Codestral-22B-v0.1 and Gemma 2 2B IT does not support tool calling, which most coding agents need.
Which has the bigger context window?
Codestral-22B-v0.1 has the largest context window at 32,768 tokens, against 8,192 for Gemma 2 2B IT. Maximum output per response: Codestral-22B-v0.1 up to 8,192, Gemma 2 2B IT up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Codestral-22B-v0.1 accepts text; Gemma 2 2B IT accepts text. They handle the same number of input types.
Are any of these open source?
Yes, both publish their weights (Mistral AI Non-Production License and Gemma Terms of Use), so you can self-host them.
Which is newer?
Gemma 2 2B IT is the newest, released Jul 31, 2024. Codestral-22B-v0.1 came out May 29, 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 Nov 2, 2026.
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