Llama-3.2-1B vs Gemma 3 4B IT vs Ministral 3B
Gemma 3 4B IT comes out ahead, 29 to 25 and 19 on our weighted score, and it is the cheaper option too.
MetaLlama-3.2-1B
19/100- ECI—
- Price$0.027 / $0.201
- Context131K
- Our pick
GoogleGemma 3 4B IT
29/100- ECI115.8
- Price$0.05 / $0.10
- Context131K
Mistral AIMinistral 3B
25/100- ECI118.0
- Price—
- Context128K
Gemma 3 4B IT is our pick
Gemma 3 4B IT is the better all-round choice, scoring 29/100 against Ministral 3B (25) and Llama-3.2-1B (19). It leads on inputs & features. The score weighs capability 67%, inputs & features 20%, context window 13%. Capability uses the average of shared benchmarks (GPQA Diamond), because Llama-3.2-1B has no Capabilities Index score yet.
- CapabilityMinistral 3BShared benchmarks: Ministral 3B 25.3% · Llama-3.2-1B 23.9% · Gemma 3 4B IT 23.2%
- Lowest priceGemma 3 4B ITGemma 3 4B IT $0.063 · Llama-3.2-1B $0.07 per 1M tokens (3:1 blend) · Ministral 3B unpriced
- Longest contextLlama-3.2-1B and Gemma 3 4B ITLlama-3.2-1B 131,072 · Gemma 3 4B IT 131,072 · Ministral 3B 128,000 tokens
- Widest inputsGemma 3 4B ITLlama-3.2-1B: Text · Gemma 3 4B IT: Text, Images · Ministral 3B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Llama-3.2-1B | Gemma 3 4B IT | Ministral 3B |
|---|---|---|---|---|
| CapabilityShared benchmarks | 67% | 24 | 23 | 25 |
| Inputs & features | 20% | 0 | 50 | 25 |
| Context window | 13% | 25 | 25 | 24 |
| Overall | 100% | 19/100 | 29/100 | 25/100 |
Left out because at least one model lacks the data: price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Llama-3.2-1BMeta | Gemma 3 4B ITGoogle | Ministral 3BMistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 115.8 | 118.0 (best) |
| ECI rank | — | #163 of 165 | #161 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 23.9% | 23.2% | 25.3% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 0.6% | 7.5% (best) | — |
| Price per million tokens | |||
| Input | $0.027 (best) | $0.05 | — |
| Output | $0.201 | $0.10 (best) | — |
| Cached input | — | — | — |
| Blended (3:1) | $0.07 | $0.063 (best) | — |
| Long-context rate | Same rate | Same rate | — |
| Price source | Median of 6 providers | Median of 9 providers | — |
| Limits | |||
| Context window | 131,072 tokens (best) | 131,072 tokens (best) | 128,000 tokens |
| Max output | 8,192 tokens | 131,072 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| 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 | 10 (best) | — |
| Released | Sep 25, 2024 | Mar 12, 2025 | Oct 16, 2024 |
| Knowledge cutoff | Dec 2023 | Aug 2024 | Mar 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
- Gemma 3 4B IT$0.70
- Ministral 3B—
Which should you choose?
Which is better: Llama-3.2-1B, Gemma 3 4B IT or Ministral 3B?
Gemma 3 4B IT is the better all-round choice, scoring 29/100 against Ministral 3B (25) and Llama-3.2-1B (19). It leads on inputs & features. The score weighs capability 67%, inputs & features 20%, context window 13%. Capability uses the average of shared benchmarks (GPQA Diamond), because Llama-3.2-1B has no Capabilities Index score yet.
Which is cheaper, Llama-3.2-1B, Gemma 3 4B IT or Ministral 3B?
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). 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). Ministral 3B has no published per-token price.
Which scores higher on benchmarks?
Not every model here has a Capabilities Index score, so we compare the average of shared benchmarks (GPQA Diamond): Ministral 3B 25.3%, Llama-3.2-1B 23.9% and Gemma 3 4B IT 23.2%. On individual benchmarks: GPQA Diamond — Ministral 3B 25.3%, Llama-3.2-1B 23.9%, Gemma 3 4B IT 23.2%.
Which is better for coding?
There are no published SWE-bench Verified results for Llama-3.2-1B, Gemma 3 4B IT and Ministral 3B yet, so there is no like-for-like coding score. On overall capability, Ministral 3B 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 and Gemma 3 4B IT have the largest context windows (131,072 and 131,072 tokens), against 128,000 for Ministral 3B. Maximum output per response: Llama-3.2-1B up to 8,192, Gemma 3 4B IT up to 131,072, Ministral 3B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Llama-3.2-1B accepts text; Gemma 3 4B IT accepts text and images; Ministral 3B accepts text. 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. Ministral 3B came out Oct 16, 2024; Llama-3.2-1B came out Sep 25, 2024. Knowledge cutoff: Llama-3.2-1B Dec 2023, Gemma 3 4B IT Aug 2024, Ministral 3B Mar 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.
