Mistral Nemo vs Qwen2.5 72B Instruct vs Llama-3.1-8B-Instruct
Too close to call on our weighted score (Mistral Nemo 48, Llama-3.1-8B-Instruct 46, Qwen2.5 72B Instruct 40). The right pick depends on what you value most.
Mistral AIMistral Nemo
48/100- ECI118.7
- Price$0.15 / $0.15
- Context128K
Alibaba (Qwen)Qwen2.5 72B Instruct
40/100- ECI129.0
- Price$1.40 / $5.60
- Context131K
MetaLlama-3.1-8B-Instruct
46/100- ECI116.6
- Price$0.152 / $0.167
- Context128K
Too close to call
It is close. Our weighted score puts them within 2 points (Mistral Nemo 48/100, Llama-3.1-8B-Instruct 46/100, Qwen2.5 72B Instruct 40/100), so choose by what matters most for your work: Qwen2.5 72B Instruct for raw capability and Mistral Nemo on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen2.5 72B InstructCapabilities Index (ECI): Qwen2.5 72B Instruct 129.0 · Mistral Nemo 118.7 · Llama-3.1-8B-Instruct 116.6
- Lowest priceMistral NemoMistral Nemo $0.15 · Llama-3.1-8B-Instruct $0.156 · Qwen2.5 72B Instruct $2.45 per 1M tokens (3:1 blend)
- Longest contextQwen2.5 72B InstructQwen2.5 72B Instruct 131,072 · Mistral Nemo 128,000 · Llama-3.1-8B-Instruct 128,000 tokens
- Widest inputsSame inputsMistral Nemo: Text · Qwen2.5 72B Instruct: Text · Llama-3.1-8B-Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Mistral Nemo | Qwen2.5 72B Instruct | Llama-3.1-8B-Instruct |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 39 | 52 | 36 |
| Price | 25% | 89 | 31 | 88 |
| Inputs & features | 15% | 25 | 25 | 25 |
| Context window | 10% | 24 | 24 | 24 |
| Overall | 100% | 48/100 | 40/100 | 46/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Mistral NemoMistral AI | Qwen2.5 72B InstructAlibaba (Qwen) | Llama-3.1-8B-InstructMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 118.7 | 129.0 (best) | 116.6 |
| ECI rank | #140 of 148 | #128 of 148 (best) | #145 of 148 |
| GPQA DiamondGraduate-level science questions | 29.9% | 49.2% (best) | 27.0% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 8.1% (best) | 1.7% |
| Price per million tokens | |||
| Input | $0.15 (best) | $1.40 | $0.152 |
| Output | $0.15 (best) | $5.60 | $0.167 |
| Cached input | — | — | — |
| Blended (3:1) | $0.15 (best) | $2.45 | $0.156 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Mistral API | Official Alibaba API | Median of 9 providers |
| Limits | |||
| Context window | 128,000 tokens | 131,072 tokens (best) | 128,000 tokens |
| Max output | 128,000 tokens (best) | 8,192 tokens | 4,096 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 | Open | Open |
| API model ID | mistral-nemo | qwen2-5-72b-instruct | — |
| API providers | 5 | 1 | 9 (best) |
| Released | Jul 1, 2024 | Sep 19, 2024 | Jul 23, 2024 |
| Knowledge cutoff | Jul 2024 | Apr 2024 | 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.
- Mistral Nemo$1.80
- Qwen2.5 72B Instruct$25.20
- Llama-3.1-8B-Instruct$1.85
Which should you choose?
Which is better: Mistral Nemo, Qwen2.5 72B Instruct or Llama-3.1-8B-Instruct?
It is close. Our weighted score puts them within 2 points (Mistral Nemo 48/100, Llama-3.1-8B-Instruct 46/100, Qwen2.5 72B Instruct 40/100), so choose by what matters most for your work: Qwen2.5 72B Instruct for raw capability and Mistral Nemo on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Mistral Nemo, Qwen2.5 72B Instruct or Llama-3.1-8B-Instruct?
Mistral Nemo 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); Qwen2.5 72B Instruct costs $1.40 input / $5.60 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.15 per million tokens for Mistral Nemo versus $0.156 for Llama-3.1-8B-Instruct (1× as much) and $2.45 for Qwen2.5 72B Instruct (16× as much).
Which scores higher on benchmarks?
Qwen2.5 72B Instruct scores higher on the Capabilities Index (ECI): Qwen2.5 72B Instruct 129.0 (#128 of 148), Mistral Nemo 118.7 (#140 of 148) and Llama-3.1-8B-Instruct 116.6 (#145 of 148). Their confidence ranges do not overlap (123.8–130.7 vs 111.3–121.5), so the gap is a real one. On individual benchmarks: GPQA Diamond — Qwen2.5 72B Instruct 49.2%, Mistral Nemo 29.9%, Llama-3.1-8B-Instruct 27.0%.
Which is better for coding?
There are no published SWE-bench Verified results for Mistral Nemo, Qwen2.5 72B Instruct and Llama-3.1-8B-Instruct yet, so there is no like-for-like coding score. On overall capability, Qwen2.5 72B 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 72B Instruct has the largest context window at 131,072 tokens, against 128,000 for Mistral Nemo and 128,000 for Llama-3.1-8B-Instruct. Maximum output per response: Mistral Nemo up to 128,000, Qwen2.5 72B Instruct up to 8,192, Llama-3.1-8B-Instruct up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Mistral Nemo accepts text; Qwen2.5 72B Instruct accepts text; Llama-3.1-8B-Instruct accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Qwen2.5 72B Instruct is the newest, released Sep 19, 2024. Llama-3.1-8B-Instruct came out Jul 23, 2024; Mistral Nemo came out Jul 1, 2024. Knowledge cutoff: Mistral Nemo Jul 2024, Qwen2.5 72B Instruct Apr 2024, 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 Oct 4, 2026.
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