Qwen2.5-VL 7B Instruct vs Aya Expanse 32B vs Codestral
Qwen2.5-VL 7B Instruct comes out ahead, 51 to 48 and 33 on our weighted score, though Codestral is 14% cheaper per token.
- Our pick
Alibaba (Qwen)Qwen2.5-VL 7B Instruct
51/100- ECI—
- Price$0.35 / $1.05
- Context131K
CohereAya Expanse 32B
33/100- ECI—
- Price$0.50 / $1.50
- Context128K
Mistral AICodestral
48/100- ECI—
- Price$0.30 / $0.90
- Context256K
Qwen2.5-VL 7B Instruct is our pick
Qwen2.5-VL 7B Instruct is the better all-round choice, scoring 51/100 against Codestral (48) and Aya Expanse 32B (33). It leads on inputs & features. Codestral wins on price and context window. 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 priceCodestralCodestral $0.45 · Qwen2.5-VL 7B Instruct $0.525 · Aya Expanse 32B $0.75 per 1M tokens (3:1 blend)
- Longest contextCodestralCodestral 256,000 · Qwen2.5-VL 7B Instruct 131,072 · Aya Expanse 32B 128,000 tokens
- Widest inputsQwen2.5-VL 7B InstructQwen2.5-VL 7B Instruct: Text, Images · Aya Expanse 32B: Text · Codestral: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen2.5-VL 7B Instruct | Aya Expanse 32B | Codestral |
|---|---|---|---|---|
| Price | 50% | 63 | 56 | 66 |
| Inputs & features | 30% | 50 | 0 | 25 |
| Context window | 20% | 24 | 24 | 36 |
| Overall | 100% | 51/100 | 33/100 | 48/100 |
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 | Qwen2.5-VL 7B InstructAlibaba (Qwen) | Aya Expanse 32BCohere | CodestralMistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.35 | $0.50 | $0.30 (best) |
| Output | $1.05 | $1.50 | $0.90 (best) |
| Cached input | — | — | $0.03 |
| Blended (3:1) | $0.525 | $0.75 | $0.45 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Median of 1 providers | Official Mistral API |
| Limits | |||
| Context window | 131,072 tokens | 128,000 tokens | 256,000 tokens (best) |
| Max output | 8,192 tokens (best) | 4,000 tokens | 4,096 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | 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 | OpenCC-BY-NC-4.0 | Open |
| API model ID | qwen2-5-vl-7b-instruct | c4ai-aya-expanse-32b | codestral-latest |
| API providers | 1 | 2 | 3 (best) |
| Released | Sep 2024 | Oct 24, 2024 | May 29, 2024 |
| Knowledge cutoff | Apr 2024 | — | Oct 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen2.5-VL 7B Instruct$5.60
- Aya Expanse 32B$8.00
- Codestral$4.80
Which should you choose?
Which is better: Qwen2.5-VL 7B Instruct, Aya Expanse 32B or Codestral?
Qwen2.5-VL 7B Instruct is the better all-round choice, scoring 51/100 against Codestral (48) and Aya Expanse 32B (33). It leads on inputs & features. Codestral wins on price and context window. 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, Qwen2.5-VL 7B Instruct, Aya Expanse 32B or Codestral?
Codestral is cheaper at $0.30 input / $0.90 output per million tokens (official Mistral API price). Qwen2.5-VL 7B Instruct costs $0.35 input / $1.05 output per million tokens (official Alibaba API price); Aya Expanse 32B costs $0.50 input / $1.50 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.45 per million tokens for Codestral versus $0.525 for Qwen2.5-VL 7B Instruct (1.2× as much) and $0.75 for Aya Expanse 32B (1.7× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen2.5-VL 7B Instruct has not been scored yet, Aya Expanse 32B has not been scored yet and Codestral has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen2.5-VL 7B Instruct, Aya Expanse 32B and Codestral yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Aya Expanse 32B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Codestral has the largest context window at 256,000 tokens, against 131,072 for Qwen2.5-VL 7B Instruct and 128,000 for Aya Expanse 32B. Maximum output per response: Qwen2.5-VL 7B Instruct up to 8,192, Aya Expanse 32B up to 4,000, Codestral up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Qwen2.5-VL 7B Instruct accepts text and images; Aya Expanse 32B accepts text; Codestral accepts text. Qwen2.5-VL 7B Instruct handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (CC-BY-NC-4.0), so you can self-host them.
Which is newer?
Aya Expanse 32B is the newest, released Oct 24, 2024. Qwen2.5-VL 7B Instruct came out Sep 2024; Codestral came out May 29, 2024. Knowledge cutoff: Qwen2.5-VL 7B Instruct Apr 2024, Codestral Oct 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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