Llama-3.1-8B-Instruct vs Voxtral Small 24B 2507 vs Qwen2.5 7B Instruct
Too close to call on our weighted score (Qwen2.5 7B Instruct 62, Llama-3.1-8B-Instruct 62, Voxtral Small 24B 2507 55). The right pick depends on what you value most.
MetaLlama-3.1-8B-Instruct
62/100- ECI116.4
- Price$0.06 / $0.08
- Context128K
Mistral AIVoxtral Small 24B 2507
55/100- ECI—
- Price$0.10 / $0.40
- Context33K
Alibaba (Qwen)Qwen2.5 7B Instruct
62/100- ECI118.5
- Price$0.05 / $0.10
- Context131K
Too close to call
It is close. Our weighted score puts them within a point (Qwen2.5 7B Instruct 62/100, Llama-3.1-8B-Instruct 62/100, Voxtral Small 24B 2507 55/100), so choose by what matters most for your work: Qwen2.5 7B Instruct on price and Qwen2.5 7B Instruct for long inputs. 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 priceQwen2.5 7B InstructQwen2.5 7B Instruct $0.063 · Llama-3.1-8B-Instruct $0.065 · Voxtral Small 24B 2507 $0.175 per 1M tokens (3:1 blend)
- Longest contextQwen2.5 7B InstructQwen2.5 7B Instruct 131,072 · Llama-3.1-8B-Instruct 128,000 · Voxtral Small 24B 2507 32,768 tokens
- Widest inputsVoxtral Small 24B 2507Llama-3.1-8B-Instruct: Text · Voxtral Small 24B 2507: Text, Audio · 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 | Voxtral Small 24B 2507 | Qwen2.5 7B Instruct |
|---|---|---|---|---|
| Price | 50% | 100 | 82 | 100 |
| Inputs & features | 30% | 25 | 45 | 25 |
| Context window | 20% | 24 | 0 | 25 |
| Overall | 100% | 62/100 | 55/100 | 62/100 |
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-3.1-8B-InstructMeta | Voxtral Small 24B 2507Mistral AI | Qwen2.5 7B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 116.4 | — | 118.5 (best) |
| ECI rank | #162 of 165 | — | #159 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 27.0% | — | 35.5% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 1.7% | — | 2.5% (best) |
| Price per million tokens | |||
| Input | $0.06 | $0.10 | $0.05 (best) |
| Output | $0.08 (best) | $0.40 | $0.10 |
| Cached input | — | — | — |
| Blended (3:1) | $0.065 | $0.175 | $0.063 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 17 providers | Official Mistral API | 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 | Yes | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | OpenApache 2.0 | OpenApache 2.0 |
| API model ID | — | voxtral-small-latest | — |
| API providers | 18 (best) | 10 | 4 |
| Released | Jul 23, 2024 | Jul 15, 2025 | 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
- Voxtral Small 24B 2507$1.80
- Qwen2.5 7B Instruct$0.70
Which should you choose?
Which is better: Llama-3.1-8B-Instruct, Voxtral Small 24B 2507 or Qwen2.5 7B Instruct?
It is close. Our weighted score puts them within a point (Qwen2.5 7B Instruct 62/100, Llama-3.1-8B-Instruct 62/100, Voxtral Small 24B 2507 55/100), so choose by what matters most for your work: Qwen2.5 7B Instruct on price and Qwen2.5 7B Instruct for long inputs. 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-3.1-8B-Instruct, Voxtral Small 24B 2507 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); Voxtral Small 24B 2507 costs $0.10 input / $0.40 output per million tokens (official Mistral API price). 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) and $0.175 for Voxtral Small 24B 2507 (2.8× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Llama-3.1-8B-Instruct has an ECI of 116.4, Voxtral Small 24B 2507 has not been scored yet and Qwen2.5 7B Instruct has an ECI of 118.5.
Which is better for coding?
There are no published SWE-bench Verified results for Llama-3.1-8B-Instruct, Voxtral Small 24B 2507 and Qwen2.5 7B Instruct yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. 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 Voxtral Small 24B 2507. Maximum output per response: Llama-3.1-8B-Instruct up to 4,096, Voxtral Small 24B 2507 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; Voxtral Small 24B 2507 accepts text and audio; Qwen2.5 7B Instruct accepts text. Voxtral Small 24B 2507 handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (Apache 2.0), so you can self-host them.
Which is newer?
Voxtral Small 24B 2507 is the newest, released Jul 15, 2025. Qwen2.5 7B Instruct came out Sep 19, 2024; Llama-3.1-8B-Instruct came out Jul 23, 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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