Command A Translate vs Pixtral Large (25.02) vs GPT-5-Codex
GPT-5-Codex comes out ahead, 42 to 33 and 17 on our weighted score, though Pixtral Large (25.02) is 13% cheaper per token.
CohereCommand A Translate
17/100- ECI—
- Price$2.50 / $10.00
- Context8K
Mistral AIPixtral Large (25.02)
33/100- ECI—
- Price$2.00 / $6.00
- Context128K
- Our pick
OpenAIGPT-5-Codex
42/100- ECI—
- Price$1.25 / $10.00
- Context400K
GPT-5-Codex is our pick
GPT-5-Codex is the better all-round choice, scoring 42/100 against Pixtral Large (25.02) (33) and Command A Translate (17). It leads on inputs & features and context window. Pixtral Large (25.02) wins on price. 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-Codex $3.44 · Command A Translate $4.38 per 1M tokens (3:1 blend)
- Longest contextGPT-5-CodexGPT-5-Codex 400,000 · Pixtral Large (25.02) 128,000 · Command A Translate 8,000 tokens
- Widest inputsPixtral Large (25.02) and GPT-5-CodexCommand A Translate: Text · Pixtral Large (25.02): Text, Images · GPT-5-Codex: Text, Images
- Self-hostingCommand A TranslatePublishes downloadable weights
| Measure | Weight | Command A Translate | Pixtral Large (25.02) | GPT-5-Codex |
|---|---|---|---|---|
| Price | 50% | 19 | 27 | 24 |
| Inputs & features | 30% | 25 | 50 | 70 |
| Context window | 20% | 0 | 24 | 44 |
| Overall | 100% | 17/100 | 33/100 | 42/100 |
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 TranslateCohere | Pixtral Large (25.02)Mistral AI | GPT-5-CodexOpenAI |
|---|---|---|---|
| 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 3 providers |
| Limits | |||
| Context window | 8,000 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 | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | command-a-translate-08-2025 | — | — |
| API providers | 1 | 3 (best) | 3 (best) |
| Released | Aug 28, 2025 | Apr 8, 2025 | Sep 15, 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 Translate$45.00
- Pixtral Large (25.02)$32.00
- GPT-5-Codex$32.50
Which should you choose?
Which is better: Command A Translate, Pixtral Large (25.02) or GPT-5-Codex?
GPT-5-Codex is the better all-round choice, scoring 42/100 against Pixtral Large (25.02) (33) and Command A Translate (17). It leads on inputs & features and context window. Pixtral Large (25.02) wins on price. 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 Translate, Pixtral Large (25.02) or GPT-5-Codex?
Pixtral Large (25.02) is cheaper at $2.00 input / $6.00 output per million tokens (median across 3 API providers). GPT-5-Codex costs $1.25 input / $10.00 output per million tokens (median across 3 API providers); Command A Translate 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-Codex (1.1× as much) and $4.38 for Command A Translate (1.5× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Command A Translate has not been scored yet, Pixtral Large (25.02) has not been scored yet and GPT-5-Codex has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Command A Translate, Pixtral Large (25.02) and GPT-5-Codex 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?
GPT-5-Codex has the largest context window at 400,000 tokens, against 128,000 for Pixtral Large (25.02) and 8,000 for Command A Translate. Maximum output per response: Command A Translate up to 8,000, Pixtral Large (25.02) up to 8,192, GPT-5-Codex up to 128,000 tokens.
Which can read images, PDFs, audio or video?
Command A Translate accepts text; Pixtral Large (25.02) accepts text and images; GPT-5-Codex accepts text and images. Pixtral Large (25.02) handles the widest range of inputs.
Are any of these open source?
Command A Translate publishes its weights and can be self-hosted; Pixtral Large (25.02) and GPT-5-Codex is proprietary.
Which is newer?
GPT-5-Codex is the newest, released Sep 15, 2025. Command A Translate came out Aug 28, 2025; Pixtral Large (25.02) came out Apr 8, 2025. Knowledge cutoff: Command A Translate Jun 1, 2024, GPT-5-Codex 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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