GPT-5 Chat vs Pixtral Large (25.02) vs Jamba Large
Too close to call on our weighted score (GPT-5 Chat 34, Pixtral Large (25.02) 33, Jamba Large 30). The right pick depends on what you value most.
OpenAIGPT-5 Chat
34/100- ECI—
- Price$1.25 / $10.00
- Context400K
Mistral AIPixtral Large (25.02)
33/100- ECI—
- Price$2.00 / $6.00
- Context128K
AI21 LabsJamba Large
30/100- ECI—
- Price$2.00 / $8.00
- Context256K
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, Jamba Large 30/100), 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 · Jamba Large $3.50 per 1M tokens (3:1 blend)
- Longest contextGPT-5 ChatGPT-5 Chat 400,000 · Jamba Large 256,000 · Pixtral Large (25.02) 128,000 tokens
- Widest inputsGPT-5 Chat and Pixtral Large (25.02)GPT-5 Chat: Text, Images · Pixtral Large (25.02): Text, Images · Jamba Large: Text
- Self-hostingJamba LargePublishes downloadable weights
| Measure | Weight | GPT-5 Chat | Pixtral Large (25.02) | Jamba Large |
|---|---|---|---|---|
| Price | 50% | 24 | 27 | 24 |
| Inputs & features | 30% | 45 | 50 | 35 |
| Context window | 20% | 44 | 24 | 36 |
| Overall | 100% | 34/100 | 33/100 | 30/100 |
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 | GPT-5 ChatOpenAI | Pixtral Large (25.02)Mistral AI | Jamba LargeAI21 Labs |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $1.25 (best) | $2.00 | $2.00 |
| Output | $10.00 | $6.00 (best) | $8.00 |
| Cached input | — | — | — |
| Blended (3:1) | $3.44 | $3.00 (best) | $3.50 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Median of 3 providers | Official AI21 Labs API |
| Limits | |||
| Context window | 400,000 tokens (best) | 128,000 tokens | 256,000 tokens |
| Max output | 128,000 tokens (best) | 8,192 tokens | 4,096 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | No |
| Tool calling | No | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Proprietary | Proprietary | Open |
| API model ID | — | — | jamba-large |
| API providers | 2 | 3 (best) | 1 |
| Released | Aug 7, 2025 | Apr 8, 2025 | Jul 1, 2025 |
| Knowledge cutoff | Sep 30, 2024 | — | Aug 22, 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GPT-5 Chat$32.50
- Pixtral Large (25.02)$32.00
- Jamba Large$36.00
Which should you choose?
Which is better: GPT-5 Chat, Pixtral Large (25.02) or Jamba Large?
It is close. Our weighted score puts them within 1 points (GPT-5 Chat 34/100, Pixtral Large (25.02) 33/100, Jamba Large 30/100), 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, GPT-5 Chat, Pixtral Large (25.02) or Jamba Large?
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); Jamba Large costs $2.00 input / $8.00 output per million tokens (official AI21 Labs 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 $3.50 for Jamba Large (1.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GPT-5 Chat has not been scored yet, Pixtral Large (25.02) has not been scored yet and Jamba Large has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5 Chat, Pixtral Large (25.02) and Jamba Large yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that 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 256,000 for Jamba Large and 128,000 for Pixtral Large (25.02). Maximum output per response: GPT-5 Chat up to 128,000, Pixtral Large (25.02) up to 8,192, Jamba Large up to 4,096 tokens.
Which can read images, PDFs, audio or video?
GPT-5 Chat accepts text and images; Pixtral Large (25.02) accepts text and images; Jamba Large accepts text. GPT-5 Chat handles the widest range of inputs.
Are any of these open source?
Jamba Large publishes its weights and can be self-hosted; GPT-5 Chat and Pixtral Large (25.02) is proprietary.
Which is newer?
GPT-5 Chat is the newest, released Aug 7, 2025. Jamba Large came out Jul 1, 2025; Pixtral Large (25.02) came out Apr 8, 2025. Knowledge cutoff: GPT-5 Chat Sep 30, 2024, Jamba Large Aug 22, 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.
