Codestral vs Qwen-VL Max vs Qwen2.5-VL 7B Instruct
Qwen2.5-VL 7B Instruct comes out ahead, 51 to 48 and 41 on our weighted score, though Codestral is 14% cheaper per token.
Mistral AICodestral
48/100- ECI—
- Price$0.30 / $0.90
- Context256K
Alibaba (Qwen)Qwen-VL Max
41/100- ECI—
- Price$0.80 / $3.20
- Context131K
- Our pickAlibaba (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 Codestral (48) and Qwen-VL Max (41). 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 · Qwen-VL Max $1.40 per 1M tokens (3:1 blend)
- Longest contextCodestralCodestral 256,000 · Qwen-VL Max 131,072 · Qwen2.5-VL 7B Instruct 131,072 tokens
- Widest inputsQwen-VL Max and Qwen2.5-VL 7B InstructCodestral: Text · Qwen-VL Max: Text, Images · Qwen2.5-VL 7B Instruct: Text, Images
- Self-hostingCodestral and Qwen2.5-VL 7B InstructPublishes downloadable weights
| Measure | Weight | Codestral | Qwen-VL Max | Qwen2.5-VL 7B Instruct |
|---|---|---|---|---|
| Price | 50% | 66 | 43 | 63 |
| Inputs & features | 30% | 25 | 50 | 50 |
| Context window | 20% | 36 | 24 | 24 |
| Overall | 100% | 48/100 | 41/100 | 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 | CodestralMistral AI | Qwen-VL MaxAlibaba (Qwen) | Qwen2.5-VL 7B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.30 (best) | $0.80 | $0.35 |
| Output | $0.90 (best) | $3.20 | $1.05 |
| Cached input | $0.03 | — | — |
| Blended (3:1) | $0.45 (best) | $1.40 | $0.525 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Mistral API | Official Alibaba API | Official Alibaba API |
| Limits | |||
| Context window | 256,000 tokens (best) | 131,072 tokens | 131,072 tokens |
| Max output | 4,096 tokens | 8,192 tokens (best) | 8,192 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| 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 | Proprietary | Open |
| API model ID | codestral-latest | qwen-vl-max | qwen2-5-vl-7b-instruct |
| API providers | 3 (best) | 3 (best) | 1 |
| Released | May 29, 2024 | Apr 8, 2024 | Sep 2024 |
| Knowledge cutoff | Oct 2024 | Apr 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
- Qwen-VL Max$14.40
- Qwen2.5-VL 7B Instruct$5.60
Which should you choose?
Which is better: Codestral, Qwen-VL Max or Qwen2.5-VL 7B Instruct?
Qwen2.5-VL 7B Instruct is the better all-round choice, scoring 51/100 against Codestral (48) and Qwen-VL Max (41). 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, Codestral, Qwen-VL Max 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); Qwen-VL Max costs $0.80 input / $3.20 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) and $1.40 for Qwen-VL Max (3.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Codestral has not been scored yet, Qwen-VL Max 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, Qwen-VL Max 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?
Codestral has the largest context window at 256,000 tokens, against 131,072 for Qwen-VL Max and 131,072 for Qwen2.5-VL 7B Instruct. Maximum output per response: Codestral up to 4,096, Qwen-VL Max up to 8,192, Qwen2.5-VL 7B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Codestral accepts text; Qwen-VL Max accepts text and images; Qwen2.5-VL 7B Instruct accepts text and images. Qwen-VL Max 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; Qwen-VL Max is proprietary.
Which is newer?
Qwen2.5-VL 7B Instruct is the newest, released Sep 2024. Codestral came out May 29, 2024; Qwen-VL Max came out Apr 8, 2024. Knowledge cutoff: Codestral Oct 2024, Qwen-VL Max Apr 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.
