Llama-3.2-1B vs Mistral 7B Instruct v0.3
Too close to call on our weighted score (Llama-3.2-1B 11, Mistral 7B Instruct v0.3 10). The right pick depends on what you value most.
MetaLlama-3.2-1B
11/100- ECI—
- Price$0.027 / $0.201
- Context131K
Mistral AIMistral 7B Instruct v0.3
10/100- ECI109.0
- Price—
- Context33K
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Make it a three-way comparison.
Too close to call
It is close. Our weighted score puts them within 1 points (Llama-3.2-1B 11/100, Mistral 7B Instruct v0.3 10/100), so choose by what matters most for your work: Llama-3.2-1B for raw capability. The score weighs capability 67%, inputs & features 20%, context window 13%. 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.
- CapabilityLlama-3.2-1BShared benchmarks: Llama-3.2-1B 12.2% · Mistral 7B Instruct v0.3 7.7%
- Lowest priceLlama-3.2-1BLlama-3.2-1B $0.07 per 1M tokens (3:1 blend) · Mistral 7B Instruct v0.3 unpriced
- Longest contextLlama-3.2-1BLlama-3.2-1B 131,072 · Mistral 7B Instruct v0.3 32,768 tokens
- Widest inputsSame inputsLlama-3.2-1B: Text · Mistral 7B Instruct v0.3: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Llama-3.2-1B | Mistral 7B Instruct v0.3 |
|---|---|---|---|
| CapabilityShared benchmarks | 67% | 12 | 8 |
| Inputs & features | 20% | 0 | 25 |
| Context window | 13% | 25 | 0 |
| Overall | 100% | 11/100 | 10/100 |
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 | Mistral 7B Instruct v0.3Mistral AI |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | 109.0 |
| ECI rank | — | #164 of 165 |
| GPQA DiamondGraduate-level science questions | 23.9% (best) | 15.2% |
| OTIS Mock AIME 2024–2025Competition mathematics | 0.6% (best) | 0.3% |
| Price per million tokens | ||
| Input | $0.027 | — |
| Output | $0.201 | — |
| Cached input | — | — |
| Blended (3:1) | $0.07 | — |
| Long-context rate | Same rate | — |
| Price source | Median of 6 providers | — |
| Limits | ||
| Context window | 131,072 tokens (best) | 32,768 tokens |
| Max output | 8,192 tokens | 32,768 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | No |
| Tool calling | No | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | OpenLlama 3.2 Community License | OpenApache 2.0 |
| API model ID | — | — |
| API providers | 6 (best) | 1 |
| Released | Sep 25, 2024 | May 22, 2024 |
| Knowledge cutoff | Dec 2023 | — |
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 7B Instruct v0.3—
Which should you choose?
Which is better: Llama-3.2-1B or Mistral 7B Instruct v0.3?
It is close. Our weighted score puts them within 1 points (Llama-3.2-1B 11/100, Mistral 7B Instruct v0.3 10/100), so choose by what matters most for your work: Llama-3.2-1B for raw capability. The score weighs capability 67%, inputs & features 20%, context window 13%. 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 or Mistral 7B Instruct v0.3?
Llama-3.2-1B is cheaper at $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 $0.07 per million tokens for Llama-3.2-1B versus . Mistral 7B Instruct v0.3 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 and OTIS Mock AIME 2024–2025): Llama-3.2-1B 12.2% and Mistral 7B Instruct v0.3 7.7%. On individual benchmarks: GPQA Diamond — Llama-3.2-1B 23.9%, Mistral 7B Instruct v0.3 15.2%; OTIS Mock AIME 2024–2025 — Llama-3.2-1B 0.6%, Mistral 7B Instruct v0.3 0.3%.
Which is better for coding?
There are no published SWE-bench Verified results for Llama-3.2-1B and Mistral 7B Instruct v0.3 yet, so there is no like-for-like coding score. On overall capability, Llama-3.2-1B 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 has the largest context window at 131,072 tokens, against 32,768 for Mistral 7B Instruct v0.3. Maximum output per response: Llama-3.2-1B up to 8,192, Mistral 7B Instruct v0.3 up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Llama-3.2-1B accepts text; Mistral 7B Instruct v0.3 accepts text. They handle the same number of input types.
Are any of these open source?
Yes, both publish their weights (Llama 3.2 Community License and Apache 2.0), so you can self-host them.
Which is newer?
Llama-3.2-1B is the newest, released Sep 25, 2024. Mistral 7B Instruct v0.3 came out May 22, 2024. Knowledge cutoff: Llama-3.2-1B Dec 2023.
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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