Codestral vs Vision Large vs Qwen2.5-VL 7B Instruct
Vision Large comes out ahead, 84 to 40 and 29 on our weighted score.
Mistral AICodestral
29/100- ECI—
- Price$0.30 / $0.90
- Context256K
- Our pick
VisparkVision Large
84/100- ECI—
- Price—
- Context1M
Alibaba (Qwen)Qwen2.5-VL 7B Instruct
40/100- ECI—
- Price$0.35 / $1.05
- Context131K
Vision Large is our pick
Vision Large is the better all-round choice, scoring 84/100 against Qwen2.5-VL 7B Instruct (40) and Codestral (29). It leads on inputs & features and context window. The score weighs inputs & features 60%, context window 40%. 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 per 1M tokens (3:1 blend) · Vision Large unpriced
- Longest contextVision LargeVision Large 1,000,000 · Codestral 256,000 · Qwen2.5-VL 7B Instruct 131,072 tokens
- Widest inputsVision LargeCodestral: Text · Vision Large: Text, Images, PDFs, Audio, Video · Qwen2.5-VL 7B Instruct: Text, Images
- Self-hostingCodestral and Qwen2.5-VL 7B InstructPublishes downloadable weights
| Measure | Weight | Codestral | Vision Large | Qwen2.5-VL 7B Instruct |
|---|---|---|---|---|
| Inputs & features | 60% | 25 | 100 | 50 |
| Context window | 40% | 36 | 60 | 24 |
| Overall | 100% | 29/100 | 84/100 | 40/100 |
Left out because at least one model lacks the data: capability and price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | CodestralMistral AI | Vision LargeVispark | Qwen2.5-VL 7B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.30 (best) | — | $0.35 |
| Output | $0.90 (best) | — | $1.05 |
| Cached input | $0.03 | — | — |
| Blended (3:1) | $0.45 (best) | — | $0.525 |
| Long-context rate | Same rate | — | Same rate |
| Price source | Official Mistral API | — | Official Alibaba API |
| Limits | |||
| Context window | 256,000 tokens | 1,000,000 tokens (best) | 131,072 tokens |
| Max output | 4,096 tokens | 65,536 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | Yes | No |
| Audio | No | Yes | No |
| Video | No | Yes | No |
| Reasoning | No | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | codestral-latest | — | qwen2-5-vl-7b-instruct |
| API providers | 3 (best) | — | 1 |
| Released | May 29, 2024 | May 15, 2024 | Sep 2024 |
| Knowledge cutoff | Oct 2024 | — | 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$4.80
- Vision Large—
- Qwen2.5-VL 7B Instruct$5.60
Which should you choose?
Which is better: Codestral, Vision Large or Qwen2.5-VL 7B Instruct?
Vision Large is the better all-round choice, scoring 84/100 against Qwen2.5-VL 7B Instruct (40) and Codestral (29). It leads on inputs & features and context window. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Codestral, Vision Large or Qwen2.5-VL 7B Instruct?
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). 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). Vision Large has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Codestral has not been scored yet, Vision Large 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, Vision Large 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. All three support tool calling for agent workflows.
Which has the bigger context window?
Vision Large has the largest context window at 1,000,000 tokens, against 256,000 for Codestral and 131,072 for Qwen2.5-VL 7B Instruct. Maximum output per response: Codestral up to 4,096, Vision Large up to 65,536, Qwen2.5-VL 7B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Codestral accepts text; Vision Large accepts text, images, PDFs, audio and video; Qwen2.5-VL 7B Instruct accepts text and images. Vision Large handles the widest range of inputs.
Are any of these open source?
Codestral and Qwen2.5-VL 7B Instruct publishes its weights and can be self-hosted; Vision Large is proprietary.
Which is newer?
Qwen2.5-VL 7B Instruct is the newest, released Sep 2024. Codestral came out May 29, 2024; Vision Large came out May 15, 2024. Knowledge cutoff: Codestral Oct 2024, 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.
