Codestral-22B-v0.1 vs Llama 3.1 Nemotron 70B Instruct vs Qwen2.5-VL 7B Instruct
Qwen2.5-VL 7B Instruct comes out ahead, 51 to 45 and 33 on our weighted score, though Codestral-22B-v0.1 is 14% cheaper per token.
Mistral AICodestral-22B-v0.1
33/100- ECI—
- Price$0.30 / $0.90
- Context33K
NVIDIALlama 3.1 Nemotron 70B Instruct
45/100- ECI—
- Price$0.478 / $0.504
- Context128K
- Our pick
Alibaba (Qwen)Qwen2.5-VL 7B Instruct
51/100- ECI—
- Price$0.35 / $1.05
- Context131K
Qwen2.5-VL 7B Instruct is our pick
Qwen2.5-VL 7B Instruct is the better all-round choice, scoring 51/100 against Llama 3.1 Nemotron 70B Instruct (45) and Codestral-22B-v0.1 (33). It leads on inputs & features. Codestral-22B-v0.1 wins on price. 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 priceCodestral-22B-v0.1Codestral-22B-v0.1 $0.45 · Llama 3.1 Nemotron 70B Instruct $0.485 · Qwen2.5-VL 7B Instruct $0.525 per 1M tokens (3:1 blend)
- Longest contextQwen2.5-VL 7B InstructQwen2.5-VL 7B Instruct 131,072 · Llama 3.1 Nemotron 70B Instruct 128,000 · Codestral-22B-v0.1 32,768 tokens
- Widest inputsQwen2.5-VL 7B InstructCodestral-22B-v0.1: Text · Llama 3.1 Nemotron 70B Instruct: Text · Qwen2.5-VL 7B Instruct: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Codestral-22B-v0.1 | Llama 3.1 Nemotron 70B Instruct | Qwen2.5-VL 7B Instruct |
|---|---|---|---|---|
| Price | 50% | 66 | 65 | 63 |
| Inputs & features | 30% | 0 | 25 | 50 |
| Context window | 20% | 0 | 24 | 24 |
| Overall | 100% | 33/100 | 45/100 | 51/100 |
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 | Codestral-22B-v0.1Mistral AI | Llama 3.1 Nemotron 70B InstructNVIDIA | Qwen2.5-VL 7B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.30 (best) | $0.478 | $0.35 |
| Output | $0.90 | $0.504 (best) | $1.05 |
| Cached input | — | — | — |
| Blended (3:1) | $0.45 (best) | $0.485 | $0.525 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Median of 2 providers | Official Alibaba API |
| Limits | |||
| Context window | 32,768 tokens | 128,000 tokens | 131,072 tokens (best) |
| Max output | 8,192 tokens | 8,192 tokens | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenMistral AI Non-Production License | Open | Open |
| API model ID | — | nvidia/llama-3.1-nemotron-70b-instruct | qwen2-5-vl-7b-instruct |
| API providers | 1 | 3 (best) | 1 |
| Released | May 29, 2024 | Apr 15, 2025 | Sep 2024 |
| Knowledge cutoff | — | — | Apr 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Codestral-22B-v0.1$4.80
- Llama 3.1 Nemotron 70B Instruct$5.79
- Qwen2.5-VL 7B Instruct$5.60
Which should you choose?
Which is better: Codestral-22B-v0.1, Llama 3.1 Nemotron 70B Instruct or Qwen2.5-VL 7B Instruct?
Qwen2.5-VL 7B Instruct is the better all-round choice, scoring 51/100 against Llama 3.1 Nemotron 70B Instruct (45) and Codestral-22B-v0.1 (33). It leads on inputs & features. Codestral-22B-v0.1 wins on price. 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, Codestral-22B-v0.1, Llama 3.1 Nemotron 70B Instruct or Qwen2.5-VL 7B Instruct?
Codestral-22B-v0.1 is cheaper at $0.30 input / $0.90 output per million tokens (median across 1 API provider). Llama 3.1 Nemotron 70B Instruct costs $0.478 input / $0.504 output per million tokens (median across 2 API providers; free on Nvidia); Qwen2.5-VL 7B Instruct costs $0.35 input / $1.05 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.45 per million tokens for Codestral-22B-v0.1 versus $0.485 for Llama 3.1 Nemotron 70B Instruct (1.1× as much) and $0.525 for Qwen2.5-VL 7B Instruct (1.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Codestral-22B-v0.1 has not been scored yet, Llama 3.1 Nemotron 70B Instruct has not been scored yet and Qwen2.5-VL 7B Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Codestral-22B-v0.1, Llama 3.1 Nemotron 70B Instruct and Qwen2.5-VL 7B Instruct yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Codestral-22B-v0.1 does not support tool calling, which most coding agents need.
Which has the bigger context window?
Qwen2.5-VL 7B Instruct has the largest context window at 131,072 tokens, against 128,000 for Llama 3.1 Nemotron 70B Instruct and 32,768 for Codestral-22B-v0.1. Maximum output per response: Codestral-22B-v0.1 up to 8,192, Llama 3.1 Nemotron 70B Instruct up to 8,192, Qwen2.5-VL 7B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Codestral-22B-v0.1 accepts text; Llama 3.1 Nemotron 70B Instruct accepts text; Qwen2.5-VL 7B Instruct accepts text and images. Qwen2.5-VL 7B Instruct handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (Mistral AI Non-Production License), so you can self-host them.
Which is newer?
Llama 3.1 Nemotron 70B Instruct is the newest, released Apr 15, 2025. Qwen2.5-VL 7B Instruct came out Sep 2024; Codestral-22B-v0.1 came out May 29, 2024. Knowledge cutoff: Qwen2.5-VL 7B Instruct Apr 2024.
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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