Command A Vision vs Pixtral Large (25.02) vs GPT-5 Chat
Too close to call on our weighted score (GPT-5 Chat 34, Pixtral Large (25.02) 33, Command A Vision 22). The right pick depends on what you value most.
CohereCommand A Vision
22/100- ECI—
- Price$2.50 / $10.00
- Context128K
Mistral AIPixtral Large (25.02)
33/100- ECI—
- Price$2.00 / $6.00
- Context128K
OpenAIGPT-5 Chat
34/100- ECI—
- Price$1.25 / $10.00
- Context400K
Too close to call
It is close. Our weighted score puts them within 1 points (GPT-5 Chat 34/100, Pixtral Large (25.02) 33/100, Command A Vision 22/100), so choose by what matters most for your work: Pixtral Large (25.02) on price and GPT-5 Chat 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 pricePixtral Large (25.02)Pixtral Large (25.02) $3.00 · GPT-5 Chat $3.44 · Command A Vision $4.38 per 1M tokens (3:1 blend)
- Longest contextGPT-5 ChatGPT-5 Chat 400,000 · Command A Vision 128,000 · Pixtral Large (25.02) 128,000 tokens
- Widest inputsSame inputsCommand A Vision: Text, Images · Pixtral Large (25.02): Text, Images · GPT-5 Chat: Text, Images
- Self-hostingCommand A VisionPublishes downloadable weights
| Measure | Weight | Command A Vision | Pixtral Large (25.02) | GPT-5 Chat |
|---|---|---|---|---|
| Price | 50% | 19 | 27 | 24 |
| Inputs & features | 30% | 25 | 50 | 45 |
| Context window | 20% | 24 | 24 | 44 |
| Overall | 100% | 22/100 | 33/100 | 34/100 |
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 | Command A VisionCohere | Pixtral Large (25.02)Mistral AI | GPT-5 ChatOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $2.50 | $2.00 | $1.25 (best) |
| Output | $10.00 | $6.00 (best) | $10.00 |
| Cached input | — | — | — |
| Blended (3:1) | $4.38 | $3.00 (best) | $3.44 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Cohere API | Median of 3 providers | Median of 2 providers |
| Limits | |||
| Context window | 128,000 tokens | 128,000 tokens | 400,000 tokens (best) |
| Max output | 8,000 tokens | 8,192 tokens | 128,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | Yes |
| Tool calling | No | Yes | No |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | command-a-vision-07-2025 | — | — |
| API providers | 1 | 3 (best) | 2 |
| Released | Jul 31, 2025 | Apr 8, 2025 | Aug 7, 2025 |
| Knowledge cutoff | Jun 1, 2024 | — | Sep 30, 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Command A Vision$45.00
- Pixtral Large (25.02)$32.00
- GPT-5 Chat$32.50
Which should you choose?
Which is better: Command A Vision, Pixtral Large (25.02) or GPT-5 Chat?
It is close. Our weighted score puts them within 1 points (GPT-5 Chat 34/100, Pixtral Large (25.02) 33/100, Command A Vision 22/100), so choose by what matters most for your work: Pixtral Large (25.02) on price and GPT-5 Chat 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, Command A Vision, Pixtral Large (25.02) or GPT-5 Chat?
Pixtral Large (25.02) is cheaper at $2.00 input / $6.00 output per million tokens (median across 3 API providers). GPT-5 Chat costs $1.25 input / $10.00 output per million tokens (median across 2 API providers); Command A Vision costs $2.50 input / $10.00 output per million tokens (official Cohere API price). At a typical mix of three input tokens to one output token, that is $3.00 per million tokens for Pixtral Large (25.02) versus $3.44 for GPT-5 Chat (1.1× as much) and $4.38 for Command A Vision (1.5× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Command A Vision has not been scored yet, Pixtral Large (25.02) has not been scored yet and GPT-5 Chat has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Command A Vision, Pixtral Large (25.02) and GPT-5 Chat yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Command A Vision and GPT-5 Chat does not support tool calling, which most coding agents need.
Which has the bigger context window?
GPT-5 Chat has the largest context window at 400,000 tokens, against 128,000 for Command A Vision and 128,000 for Pixtral Large (25.02). Maximum output per response: Command A Vision up to 8,000, Pixtral Large (25.02) up to 8,192, GPT-5 Chat up to 128,000 tokens.
Which can read images, PDFs, audio or video?
Command A Vision accepts text and images; Pixtral Large (25.02) accepts text and images; GPT-5 Chat accepts text and images. They handle the same number of input types.
Are any of these open source?
Command A Vision publishes its weights and can be self-hosted; Pixtral Large (25.02) and GPT-5 Chat is proprietary.
Which is newer?
GPT-5 Chat is the newest, released Aug 7, 2025. Command A Vision came out Jul 31, 2025; Pixtral Large (25.02) came out Apr 8, 2025. Knowledge cutoff: Command A Vision Jun 1, 2024, GPT-5 Chat Sep 30, 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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