Llama-3.1-8B-Instruct vs Mistral 7B Instruct v0.3 vs Qwen2.5 7B Instruct
Too close to call on our weighted score (Qwen2.5 7B Instruct 33, Llama-3.1-8B-Instruct 32, Mistral 7B Instruct v0.3 22). The right pick depends on what you value most.
MetaLlama-3.1-8B-Instruct
32/100- ECI116.4
- Price$0.06 / $0.08
- Context128K
Mistral AIMistral 7B Instruct v0.3
22/100- ECI109.0
- Price—
- Context33K
Alibaba (Qwen)Qwen2.5 7B Instruct
33/100- ECI118.5
- Price$0.05 / $0.10
- Context131K
Too close to call
It is close. Our weighted score puts them within 2 points (Qwen2.5 7B Instruct 33/100, Llama-3.1-8B-Instruct 32/100, Mistral 7B Instruct v0.3 22/100), so choose by what matters most for your work: Qwen2.5 7B Instruct for raw capability. The score weighs capability 67%, inputs & features 20%, context window 13%.
- CapabilityQwen2.5 7B InstructCapabilities Index (ECI): Qwen2.5 7B Instruct 118.5 · Llama-3.1-8B-Instruct 116.4 · Mistral 7B Instruct v0.3 109.0
- Lowest priceQwen2.5 7B InstructQwen2.5 7B Instruct $0.063 · Llama-3.1-8B-Instruct $0.065 per 1M tokens (3:1 blend) · Mistral 7B Instruct v0.3 unpriced
- Longest contextQwen2.5 7B InstructQwen2.5 7B Instruct 131,072 · Llama-3.1-8B-Instruct 128,000 · Mistral 7B Instruct v0.3 32,768 tokens
- Widest inputsSame inputsLlama-3.1-8B-Instruct: Text · Mistral 7B Instruct v0.3: Text · Qwen2.5 7B Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Llama-3.1-8B-Instruct | Mistral 7B Instruct v0.3 | Qwen2.5 7B Instruct |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 67% | 35 | 26 | 38 |
| Inputs & features | 20% | 25 | 25 | 25 |
| Context window | 13% | 24 | 0 | 25 |
| Overall | 100% | 32/100 | 22/100 | 33/100 |
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-3.1-8B-InstructMeta | Mistral 7B Instruct v0.3Mistral AI | Qwen2.5 7B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 116.4 | 109.0 | 118.5 (best) |
| ECI rank | #162 of 165 | #164 of 165 | #159 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 27.0% | 15.2% | 35.5% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 1.7% | 0.3% | 2.5% (best) |
| Price per million tokens | |||
| Input | $0.06 | — | $0.05 (best) |
| Output | $0.08 (best) | — | $0.10 |
| Cached input | — | — | — |
| Blended (3:1) | $0.065 | — | $0.063 (best) |
| Long-context rate | Same rate | — | Same rate |
| Price source | Median of 17 providers | — | Median of 3 providers |
| Limits | |||
| Context window | 128,000 tokens | 32,768 tokens | 131,072 tokens (best) |
| Max output | 4,096 tokens | 32,768 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | OpenApache 2.0 | OpenApache 2.0 |
| API model ID | — | — | — |
| API providers | 18 (best) | 1 | 4 |
| Released | Jul 23, 2024 | May 22, 2024 | Sep 19, 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-3.1-8B-Instruct$0.76
- Mistral 7B Instruct v0.3—
- Qwen2.5 7B Instruct$0.70
Which should you choose?
Which is better: Llama-3.1-8B-Instruct, Mistral 7B Instruct v0.3 or Qwen2.5 7B Instruct?
It is close. Our weighted score puts them within 2 points (Qwen2.5 7B Instruct 33/100, Llama-3.1-8B-Instruct 32/100, Mistral 7B Instruct v0.3 22/100), so choose by what matters most for your work: Qwen2.5 7B Instruct for raw capability. The score weighs capability 67%, inputs & features 20%, context window 13%.
Which is cheaper, Llama-3.1-8B-Instruct, Mistral 7B Instruct v0.3 or Qwen2.5 7B Instruct?
Qwen2.5 7B Instruct is cheaper at $0.05 input / $0.10 output per million tokens (median across 3 API providers). Llama-3.1-8B-Instruct costs $0.06 input / $0.08 output per million tokens (median across 17 API providers). At a typical mix of three input tokens to one output token, that is $0.063 per million tokens for Qwen2.5 7B Instruct versus $0.065 for Llama-3.1-8B-Instruct (1× as much). Mistral 7B Instruct v0.3 has no published per-token price.
Which scores higher on benchmarks?
Qwen2.5 7B Instruct scores higher on the Capabilities Index (ECI): Qwen2.5 7B Instruct 118.5 (#159 of 165), Llama-3.1-8B-Instruct 116.4 (#162 of 165) and Mistral 7B Instruct v0.3 109.0 (#164 of 165). The confidence ranges of the top two overlap (110.6–121.2 vs 107.0–121.6), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen2.5 7B Instruct 35.5%, Llama-3.1-8B-Instruct 27.0%, Mistral 7B Instruct v0.3 15.2%; OTIS Mock AIME 2024–2025 — Qwen2.5 7B Instruct 2.5%, Llama-3.1-8B-Instruct 1.7%, Mistral 7B Instruct v0.3 0.3%.
Which is better for coding?
There are no published SWE-bench Verified results for Llama-3.1-8B-Instruct, Mistral 7B Instruct v0.3 and Qwen2.5 7B Instruct yet, so there is no like-for-like coding score. On overall capability, Qwen2.5 7B Instruct leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
Qwen2.5 7B Instruct has the largest context window at 131,072 tokens, against 128,000 for Llama-3.1-8B-Instruct and 32,768 for Mistral 7B Instruct v0.3. Maximum output per response: Llama-3.1-8B-Instruct up to 4,096, Mistral 7B Instruct v0.3 up to 32,768, Qwen2.5 7B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Llama-3.1-8B-Instruct accepts text; Mistral 7B Instruct v0.3 accepts text; Qwen2.5 7B Instruct accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (Apache 2.0), so you can self-host them.
Which is newer?
Qwen2.5 7B Instruct is the newest, released Sep 19, 2024. Llama-3.1-8B-Instruct came out Jul 23, 2024; Mistral 7B Instruct v0.3 came out May 22, 2024. Knowledge cutoff: Llama-3.1-8B-Instruct 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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