Llama-3.1-8B-Instruct vs Mixtral 8x7B Instruct v0.1 vs Qwen2.5 7B Instruct
Too close to call on our weighted score (Qwen2.5 7B Instruct 50, Llama-3.1-8B-Instruct 49, Mixtral 8x7B Instruct v0.1 34). The right pick depends on what you value most.
MetaLlama-3.1-8B-Instruct
49/100- ECI116.4
- Price$0.06 / $0.08
- Context128K
Mistral AIMixtral 8x7B Instruct v0.1
34/100- ECI118.4
- Price$0.444 / $0.579
- Context33K
Alibaba (Qwen)Qwen2.5 7B Instruct
50/100- ECI118.5
- Price$0.05 / $0.10
- Context131K
Too close to call
It is close. Our weighted score puts them within 1 points (Qwen2.5 7B Instruct 50/100, Llama-3.1-8B-Instruct 49/100, Mixtral 8x7B Instruct v0.1 34/100), so choose by what matters most for your work: Qwen2.5 7B Instruct for raw capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen2.5 7B InstructCapabilities Index (ECI): Qwen2.5 7B Instruct 118.5 · Mixtral 8x7B Instruct v0.1 118.4 · Llama-3.1-8B-Instruct 116.4
- Lowest priceQwen2.5 7B InstructQwen2.5 7B Instruct $0.063 · Llama-3.1-8B-Instruct $0.065 · Mixtral 8x7B Instruct v0.1 $0.478 per 1M tokens (3:1 blend)
- Longest contextQwen2.5 7B InstructQwen2.5 7B Instruct 131,072 · Llama-3.1-8B-Instruct 128,000 · Mixtral 8x7B Instruct v0.1 32,768 tokens
- Widest inputsSame inputsLlama-3.1-8B-Instruct: Text · Mixtral 8x7B Instruct v0.1: 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 | Mixtral 8x7B Instruct v0.1 | Qwen2.5 7B Instruct |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 35 | 38 | 38 |
| Price | 25% | 100 | 62 | 100 |
| Inputs & features | 15% | 25 | 0 | 25 |
| Context window | 10% | 24 | 0 | 25 |
| Overall | 100% | 49/100 | 34/100 | 50/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Llama-3.1-8B-InstructMeta | Mixtral 8x7B Instruct v0.1Mistral AI | Qwen2.5 7B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 116.4 | 118.4 | 118.5 (best) |
| ECI rank | #162 of 165 | #160 of 165 | #159 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 27.0% | 30.6% | 35.5% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 1.7% | — | 2.5% (best) |
| Price per million tokens | |||
| Input | $0.06 | $0.444 | $0.05 (best) |
| Output | $0.08 (best) | $0.579 | $0.10 |
| Cached input | — | — | — |
| Blended (3:1) | $0.065 | $0.478 | $0.063 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 17 providers | Median of 2 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 | No | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | OpenApache 2.0 | OpenApache 2.0 |
| API model ID | — | — | — |
| API providers | 18 (best) | 2 | 4 |
| Released | Jul 23, 2024 | Dec 11, 2023 | 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
- Mixtral 8x7B Instruct v0.1$5.60
- Qwen2.5 7B Instruct$0.70
Which should you choose?
Which is better: Llama-3.1-8B-Instruct, Mixtral 8x7B Instruct v0.1 or Qwen2.5 7B Instruct?
It is close. Our weighted score puts them within 1 points (Qwen2.5 7B Instruct 50/100, Llama-3.1-8B-Instruct 49/100, Mixtral 8x7B Instruct v0.1 34/100), so choose by what matters most for your work: Qwen2.5 7B Instruct for raw capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Llama-3.1-8B-Instruct, Mixtral 8x7B Instruct v0.1 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); Mixtral 8x7B Instruct v0.1 costs $0.444 input / $0.579 output per million tokens (median across 2 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) and $0.478 for Mixtral 8x7B Instruct v0.1 (7.6× as much).
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), Mixtral 8x7B Instruct v0.1 118.4 (#160 of 165) and Llama-3.1-8B-Instruct 116.4 (#162 of 165). The confidence ranges of the top two overlap (110.6–121.2 vs 110.9–121.0), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen2.5 7B Instruct 35.5%, Mixtral 8x7B Instruct v0.1 30.6%, Llama-3.1-8B-Instruct 27.0%.
Which is better for coding?
There are no published SWE-bench Verified results for Llama-3.1-8B-Instruct, Mixtral 8x7B Instruct v0.1 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. Note that Mixtral 8x7B Instruct v0.1 does not support tool calling, which most coding agents need.
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 Mixtral 8x7B Instruct v0.1. Maximum output per response: Llama-3.1-8B-Instruct up to 4,096, Mixtral 8x7B Instruct v0.1 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; Mixtral 8x7B Instruct v0.1 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; Mixtral 8x7B Instruct v0.1 came out Dec 11, 2023. 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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