Mistral Nemo vs Llama-3.2-1B vs Qwen2.5 7B Instruct
Qwen2.5 7B Instruct comes out ahead, 49 to 43 and 39 on our weighted score, and it is the cheaper option too.
Mistral AIMistral Nemo
43/100- ECI118.6
- Price$0.12 / $0.13
- Context128K
MetaLlama-3.2-1B
39/100- ECI—
- Price$0.027 / $0.201
- Context131K
- Our pick
Alibaba (Qwen)Qwen2.5 7B Instruct
49/100- ECI118.5
- Price$0.05 / $0.10
- Context131K
Qwen2.5 7B Instruct is our pick
Qwen2.5 7B Instruct is the better all-round choice, scoring 49/100 against Mistral Nemo (43) and Llama-3.2-1B (39). It leads on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (GPQA Diamond), because Llama-3.2-1B has no Capabilities Index score yet.
- CapabilityQwen2.5 7B InstructShared benchmarks: Qwen2.5 7B Instruct 35.5% · Mistral Nemo 29.9% · Llama-3.2-1B 23.9%
- Lowest priceQwen2.5 7B InstructQwen2.5 7B Instruct $0.063 · Llama-3.2-1B $0.07 · Mistral Nemo $0.123 per 1M tokens (3:1 blend)
- Longest contextLlama-3.2-1B and Qwen2.5 7B InstructLlama-3.2-1B 131,072 · Qwen2.5 7B Instruct 131,072 · Mistral Nemo 128,000 tokens
- Widest inputsSame inputsMistral Nemo: Text · Llama-3.2-1B: Text · Qwen2.5 7B Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Mistral Nemo | Llama-3.2-1B | Qwen2.5 7B Instruct |
|---|---|---|---|---|
| CapabilityShared benchmarks | 50% | 30 | 24 | 35 |
| Price | 25% | 89 | 100 | 100 |
| Inputs & features | 15% | 25 | 0 | 25 |
| Context window | 10% | 24 | 25 | 25 |
| Overall | 100% | 43/100 | 39/100 | 49/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Mistral NemoMistral AI | Llama-3.2-1BMeta | Qwen2.5 7B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 118.6 (best) | — | 118.5 |
| ECI rank | #157 of 165 (best) | — | #159 of 165 |
| GPQA DiamondGraduate-level science questions | 29.9% | 23.9% | 35.5% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 0.6% | 2.5% (best) |
| Price per million tokens | |||
| Input | $0.12 | $0.027 (best) | $0.05 |
| Output | $0.13 | $0.201 | $0.10 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.123 | $0.07 | $0.063 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 12 providers | Median of 6 providers | Median of 3 providers |
| Limits | |||
| Context window | 128,000 tokens | 131,072 tokens (best) | 131,072 tokens (best) |
| Max output | 128,000 tokens (best) | 8,192 tokens | 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 | OpenLlama 3.2 Community License | OpenApache 2.0 |
| API model ID | — | — | — |
| API providers | 12 (best) | 6 | 4 |
| Released | Jul 18, 2024 | Sep 25, 2024 | Sep 19, 2024 |
| Knowledge cutoff | Jul 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$1.46
- Llama-3.2-1B$0.672
- Qwen2.5 7B Instruct$0.70
Which should you choose?
Which is better: Mistral Nemo, Llama-3.2-1B or Qwen2.5 7B Instruct?
Qwen2.5 7B Instruct is the better all-round choice, scoring 49/100 against Mistral Nemo (43) and Llama-3.2-1B (39). It leads on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (GPQA Diamond), because Llama-3.2-1B has no Capabilities Index score yet.
Which is cheaper, Mistral Nemo, Llama-3.2-1B 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-3.2-1B costs $0.027 input / $0.201 output per million tokens (median across 6 API providers); Mistral Nemo costs $0.12 input / $0.13 output per million tokens (median across 12 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 $0.07 for Llama-3.2-1B (1.1× as much) and $0.123 for Mistral Nemo (2× as much).
Which scores higher on benchmarks?
Not every model here has a Capabilities Index score, so we compare the average of shared benchmarks (GPQA Diamond): Qwen2.5 7B Instruct 35.5%, Mistral Nemo 29.9% and Llama-3.2-1B 23.9%. On individual benchmarks: GPQA Diamond — Qwen2.5 7B Instruct 35.5%, Mistral Nemo 29.9%, Llama-3.2-1B 23.9%.
Which is better for coding?
There are no published SWE-bench Verified results for Mistral Nemo, Llama-3.2-1B 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 Llama-3.2-1B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Llama-3.2-1B and Qwen2.5 7B Instruct have the largest context windows (131,072 and 131,072 tokens), against 128,000 for Mistral Nemo. Maximum output per response: Mistral Nemo up to 128,000, Llama-3.2-1B up to 8,192, Qwen2.5 7B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Mistral Nemo accepts text; Llama-3.2-1B 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 (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. Qwen2.5 7B Instruct came out Sep 19, 2024; Mistral Nemo came out Jul 18, 2024. Knowledge cutoff: Mistral Nemo Jul 2024, 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.
