Codestral vs Qwen Plus vs Qwen2.5-VL 7B Instruct
Too close to call on our weighted score (Qwen Plus 53, Qwen2.5-VL 7B Instruct 51, Codestral 48). The right pick depends on what you value most.
Mistral AICodestral
48/100- ECI—
- Price$0.30 / $0.90
- Context256K
Alibaba (Qwen)Qwen Plus
53/100- ECI—
- Price$0.40 / $1.20
- Context1M
- Alibaba (Qwen)
Qwen2.5-VL 7B Instruct
51/100- ECI—
- Price$0.35 / $1.05
- Context131K
Too close to call
It is close. Our weighted score puts them within 1 points (Qwen Plus 53/100, Qwen2.5-VL 7B Instruct 51/100, Codestral 48/100), so choose by what matters most for your work: Codestral on price and Qwen Plus for long inputs. 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 Plus $0.60 per 1M tokens (3:1 blend)
- Longest contextQwen PlusQwen Plus 1,000,000 · Codestral 256,000 · Qwen2.5-VL 7B Instruct 131,072 tokens
- Widest inputsQwen2.5-VL 7B InstructCodestral: Text · Qwen Plus: Text · Qwen2.5-VL 7B Instruct: Text, Images
- Self-hostingCodestral and Qwen2.5-VL 7B InstructPublishes downloadable weights
| Measure | Weight | Codestral | Qwen Plus | Qwen2.5-VL 7B Instruct |
|---|---|---|---|---|
| Price | 50% | 66 | 60 | 63 |
| Inputs & features | 30% | 25 | 35 | 50 |
| Context window | 20% | 36 | 60 | 24 |
| Overall | 100% | 48/100 | 53/100 | 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 PlusAlibaba (Qwen) | Qwen2.5-VL 7B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.30 (best) | $0.40 | $0.35 |
| Output | $0.90 (best) | $1.20 | $1.05 |
| Cached input | $0.03 | — | — |
| Blended (3:1) | $0.45 (best) | $0.60 | $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 | 1,000,000 tokens (best) | 131,072 tokens |
| Max output | 4,096 tokens | 32,768 tokens (best) | 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 | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | codestral-latest | qwen-plus | qwen2-5-vl-7b-instruct |
| API providers | 3 | 9 (best) | 1 |
| Released | May 29, 2024 | Jan 25, 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 Plus$6.40
- Qwen2.5-VL 7B Instruct$5.60
Which should you choose?
Which is better: Codestral, Qwen Plus or Qwen2.5-VL 7B Instruct?
It is close. Our weighted score puts them within 1 points (Qwen Plus 53/100, Qwen2.5-VL 7B Instruct 51/100, Codestral 48/100), so choose by what matters most for your work: Codestral on price and Qwen Plus for long inputs. 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 Plus 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 Plus costs $0.40 input / $1.20 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 $0.60 for Qwen Plus (1.3× 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 Plus 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 Plus 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?
Qwen Plus 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, Qwen Plus up to 32,768, Qwen2.5-VL 7B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Codestral accepts text; Qwen Plus 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?
Codestral and Qwen2.5-VL 7B Instruct publishes its weights and can be self-hosted; Qwen Plus is proprietary.
Which is newer?
Qwen2.5-VL 7B Instruct is the newest, released Sep 2024. Codestral came out May 29, 2024; Qwen Plus came out Jan 25, 2024. Knowledge cutoff: Codestral Oct 2024, Qwen Plus 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.
