Llama-3.2-1B vs Mistral Large 2.1 vs Gemma 3 4B IT
Gemma 3 4B IT comes out ahead, 43 to 34 and 27 on our weighted score, and it is the cheaper option too.
MetaLlama-3.2-1B
34/100- ECI—
- Price$0.027 / $0.201
- Context131K
Mistral AIMistral Large 2.1
27/100- ECI128.6
- Price$2.00 / $6.00
- Context131K
- Our pick
GoogleGemma 3 4B IT
43/100- ECI115.8
- Price$0.05 / $0.10
- Context131K
Gemma 3 4B IT is our pick
Gemma 3 4B IT is the better all-round choice, scoring 43/100 against Llama-3.2-1B (34) and Mistral Large 2.1 (27). It leads on inputs & features. Mistral Large 2.1 wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025), because Llama-3.2-1B has no Capabilities Index score yet.
- CapabilityMistral Large 2.1Shared benchmarks: Mistral Large 2.1 29.6% · Gemma 3 4B IT 15.4% · Llama-3.2-1B 12.2%
- Lowest priceGemma 3 4B ITGemma 3 4B IT $0.063 · Llama-3.2-1B $0.07 · Mistral Large 2.1 $3.00 per 1M tokens (3:1 blend)
- Longest contextAbout the sameLlama-3.2-1B 131,072 · Mistral Large 2.1 131,072 · Gemma 3 4B IT 131,072 tokens
- Widest inputsGemma 3 4B ITLlama-3.2-1B: Text · Mistral Large 2.1: Text · Gemma 3 4B IT: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Llama-3.2-1B | Mistral Large 2.1 | Gemma 3 4B IT |
|---|---|---|---|---|
| CapabilityShared benchmarks | 50% | 12 | 30 | 15 |
| Price | 25% | 100 | 26 | 100 |
| Inputs & features | 15% | 0 | 25 | 50 |
| Context window | 10% | 25 | 25 | 25 |
| Overall | 100% | 34/100 | 27/100 | 43/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Llama-3.2-1BMeta | Mistral Large 2.1Mistral AI | Gemma 3 4B ITGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 128.6 (best) | 115.8 |
| ECI rank | — | #142 of 165 (best) | #163 of 165 |
| GPQA DiamondGraduate-level science questions | 23.9% | 51.3% (best) | 23.2% |
| OTIS Mock AIME 2024–2025Competition mathematics | 0.6% | 7.8% (best) | 7.5% |
| Price per million tokens | |||
| Input | $0.027 (best) | $2.00 | $0.05 |
| Output | $0.201 | $6.00 | $0.10 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.07 | $3.00 | $0.063 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 6 providers | Median of 2 providers | Median of 9 providers |
| Limits | |||
| Context window | 131,072 tokens | 131,072 tokens | 131,072 tokens |
| Max output | 8,192 tokens | 16,384 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenLlama 3.2 Community License | Open | Open |
| API model ID | — | — | — |
| API providers | 6 | 2 | 10 (best) |
| Released | Sep 25, 2024 | Nov 18, 2024 | Mar 12, 2025 |
| Knowledge cutoff | Dec 2023 | Nov 2024 | Aug 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Llama-3.2-1B$0.672
- Mistral Large 2.1$32.00
- Gemma 3 4B IT$0.70
Which should you choose?
Which is better: Llama-3.2-1B, Mistral Large 2.1 or Gemma 3 4B IT?
Gemma 3 4B IT is the better all-round choice, scoring 43/100 against Llama-3.2-1B (34) and Mistral Large 2.1 (27). It leads on inputs & features. Mistral Large 2.1 wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025), because Llama-3.2-1B has no Capabilities Index score yet.
Which is cheaper, Llama-3.2-1B, Mistral Large 2.1 or Gemma 3 4B IT?
Gemma 3 4B IT is cheaper at $0.05 input / $0.10 output per million tokens (median across 9 API providers). Llama-3.2-1B costs $0.027 input / $0.201 output per million tokens (median across 6 API providers); Mistral Large 2.1 costs $2.00 input / $6.00 output per million tokens (median across 2 API providers). At a typical mix of three input tokens to one output token, that is $0.063 per million tokens for Gemma 3 4B IT versus $0.07 for Llama-3.2-1B (1.1× as much) and $3.00 for Mistral Large 2.1 (48× as much).
Which scores higher on benchmarks?
Not every model here has a Capabilities Index score, so we compare the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025): Mistral Large 2.1 29.6%, Gemma 3 4B IT 15.4% and Llama-3.2-1B 12.2%. On individual benchmarks: GPQA Diamond — Mistral Large 2.1 51.3%, Llama-3.2-1B 23.9%, Gemma 3 4B IT 23.2%; OTIS Mock AIME 2024–2025 — Mistral Large 2.1 7.8%, Gemma 3 4B IT 7.5%, Llama-3.2-1B 0.6%.
Which is better for coding?
There are no published SWE-bench Verified results for Llama-3.2-1B, Mistral Large 2.1 and Gemma 3 4B IT yet, so there is no like-for-like coding score. On overall capability, Mistral Large 2.1 leads, which tends to carry over to coding, but test on your own codebase. Note that Llama-3.2-1B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Llama-3.2-1B, Mistral Large 2.1 and Gemma 3 4B IT share the same 131,072-token context window. Maximum output per response: Llama-3.2-1B up to 8,192, Mistral Large 2.1 up to 16,384, Gemma 3 4B IT up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Llama-3.2-1B accepts text; Mistral Large 2.1 accepts text; Gemma 3 4B IT accepts text and images. Gemma 3 4B IT handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (Llama 3.2 Community License), so you can self-host them.
Which is newer?
Gemma 3 4B IT is the newest, released Mar 12, 2025. Mistral Large 2.1 came out Nov 18, 2024; Llama-3.2-1B came out Sep 25, 2024. Knowledge cutoff: Llama-3.2-1B Dec 2023, Mistral Large 2.1 Nov 2024, Gemma 3 4B IT Aug 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.
