Llama-3.2-3B vs Llama-3.1-8B-Instruct vs Pixtral 12B
Pixtral 12B comes out ahead, 64 to 56 and 49 on our weighted score, and it is the cheaper option too.
MetaLlama-3.2-3B
49/100- ECI—
- Price$0.10 / $0.335
- Context131K
- Meta
Llama-3.1-8B-Instruct
56/100- ECI116.6
- Price$0.152 / $0.167
- Context128K
- 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 Llama-3.1-8B-Instruct (56) and Llama-3.2-3B (49). It leads on inputs & features. 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-3.1-8B-Instruct $0.156 · Llama-3.2-3B $0.159 per 1M tokens (3:1 blend)
- Longest contextLlama-3.2-3BLlama-3.2-3B 131,072 · Llama-3.1-8B-Instruct 128,000 · Pixtral 12B 128,000 tokens
- Widest inputsPixtral 12BLlama-3.2-3B: Text · Llama-3.1-8B-Instruct: Text · Pixtral 12B: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Llama-3.2-3B | Llama-3.1-8B-Instruct | Pixtral 12B |
|---|---|---|---|---|
| Price | 50% | 88 | 88 | 89 |
| Inputs & features | 30% | 0 | 25 | 50 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 49/100 | 56/100 | 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 | Llama-3.2-3BMeta | Llama-3.1-8B-InstructMeta | Pixtral 12BMistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 116.6 | — |
| ECI rank | — | #145 of 148 | — |
| GPQA DiamondGraduate-level science questions | — | 27.0% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 1.7% | — |
| Price per million tokens | |||
| Input | $0.10 (best) | $0.152 | $0.15 |
| Output | $0.335 | $0.167 | $0.15 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.159 | $0.156 | $0.15 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 3 providers | Median of 9 providers | Official Mistral API |
| Limits | |||
| Context window | 131,072 tokens (best) | 128,000 tokens | 128,000 tokens |
| Max output | 8,192 tokens | 4,096 tokens | 128,000 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 | — | — | pixtral-12b |
| API providers | 3 | 9 (best) | 4 |
| Released | Sep 25, 2024 | Jul 23, 2024 | Sep 1, 2024 |
| Knowledge cutoff | Dec 2023 | 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.
- Llama-3.2-3B$1.67
- Llama-3.1-8B-Instruct$1.85
- Pixtral 12B$1.80
Which should you choose?
Which is better: Llama-3.2-3B, Llama-3.1-8B-Instruct or Pixtral 12B?
Pixtral 12B is the better all-round choice, scoring 64/100 against Llama-3.1-8B-Instruct (56) and Llama-3.2-3B (49). It leads on inputs & features. 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, Llama-3.2-3B, Llama-3.1-8B-Instruct or Pixtral 12B?
Pixtral 12B is cheaper at $0.15 input / $0.15 output per million tokens (official Mistral API price). Llama-3.1-8B-Instruct costs $0.152 input / $0.167 output per million tokens (median across 9 API providers); Llama-3.2-3B costs $0.10 input / $0.335 output per million tokens (median across 3 API providers). At a typical mix of three input tokens to one output token, that is $0.15 per million tokens for Pixtral 12B versus $0.156 for Llama-3.1-8B-Instruct (1× as much) and $0.159 for Llama-3.2-3B (1.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Llama-3.2-3B has not been scored yet, Llama-3.1-8B-Instruct has an ECI of 116.6 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, Llama-3.1-8B-Instruct 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?
Llama-3.2-3B has the largest context window at 131,072 tokens, against 128,000 for Llama-3.1-8B-Instruct and 128,000 for Pixtral 12B. Maximum output per response: Llama-3.2-3B up to 8,192, Llama-3.1-8B-Instruct up to 4,096, Pixtral 12B up to 128,000 tokens.
Which can read images, PDFs, audio or video?
Llama-3.2-3B accepts text; Llama-3.1-8B-Instruct accepts text; Pixtral 12B accepts text and images. Pixtral 12B 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?
Llama-3.2-3B is the newest, released Sep 25, 2024. Pixtral 12B came out Sep 1, 2024; Llama-3.1-8B-Instruct came out Jul 23, 2024. Knowledge cutoff: Llama-3.2-3B Dec 2023, Llama-3.1-8B-Instruct 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.
