GPT-5 Chat vs Jamba Large vs o4-mini-deep-research
o4-mini-deep-research comes out ahead, 37 to 31 and 29 on our weighted score, and it is the cheaper option too.
OpenAIGPT-5 Chat
31/100- ECI—
- Price$1.25 / $10.00
- Context400K
AI21 LabsJamba Large
29/100- ECI—
- Price$2.00 / $8.00
- Context256K
- Our pickOpenAI
o4-mini-deep-research
37/100- ECI—
- Price$1.80 / $7.20
- Context200K
o4-mini-deep-research is our pick
o4-mini-deep-research is the better all-round choice, scoring 37/100 against GPT-5 Chat (31) and Jamba Large (29). It leads on price and inputs & features. GPT-5 Chat wins on 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 priceo4-mini-deep-researcho4-mini-deep-research $3.15 · 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 · o4-mini-deep-research 200,000 tokens
- Widest inputsGPT-5 Chat and o4-mini-deep-researchGPT-5 Chat: Text, Images · Jamba Large: Text · o4-mini-deep-research: Text, Images
- Self-hostingJamba LargePublishes downloadable weights
| Measure | Weight | GPT-5 Chat | Jamba Large | o4-mini-deep-research |
|---|---|---|---|---|
| Price | 50% | 23 | 23 | 25 |
| Inputs & features | 30% | 35 | 35 | 60 |
| Context window | 20% | 44 | 36 | 32 |
| Overall | 100% | 31/100 | 29/100 | 37/100 |
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 | Jamba LargeAI21 Labs | o4-mini-deep-researchOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $1.25 (best) | $2.00 | $1.80 |
| Output | $10.00 | $8.00 | $7.20 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $3.44 | $3.50 | $3.15 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 5 providers | Official AI21 Labs API | Median of 1 providers |
| Limits | |||
| Context window | 400,000 tokens (best) | 256,000 tokens | 200,000 tokens |
| Max output | 128,000 tokens (best) | 4,096 tokens | 100,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | Yes |
| Tool calling | No | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | — | jamba-large | — |
| API providers | 5 (best) | 1 | 1 |
| Released | Aug 7, 2025 | Jul 3, 2025 | Jun 26, 2025 |
| Knowledge cutoff | Sep 30, 2024 | Aug 22, 2024 | May 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
- Jamba Large$36.00
- o4-mini-deep-research$32.40
Which should you choose?
Which is better: GPT-5 Chat, Jamba Large or o4-mini-deep-research?
o4-mini-deep-research is the better all-round choice, scoring 37/100 against GPT-5 Chat (31) and Jamba Large (29). It leads on price and inputs & features. GPT-5 Chat wins on 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, GPT-5 Chat, Jamba Large or o4-mini-deep-research?
o4-mini-deep-research is cheaper at $1.80 input / $7.20 output per million tokens (median across 1 API provider). GPT-5 Chat costs $1.25 input / $10.00 output per million tokens (median across 5 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 $3.15 per million tokens for o4-mini-deep-research versus $3.44 for GPT-5 Chat (1.1× as much) and $3.50 for Jamba Large (1.1× 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, Jamba Large has not been scored yet and o4-mini-deep-research has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5 Chat, Jamba Large and o4-mini-deep-research 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 200,000 for o4-mini-deep-research. Maximum output per response: GPT-5 Chat up to 128,000, Jamba Large up to 4,096, o4-mini-deep-research up to 100,000 tokens.
Which can read images, PDFs, audio or video?
GPT-5 Chat accepts text and images; Jamba Large accepts text; o4-mini-deep-research accepts text and images. 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 o4-mini-deep-research is proprietary.
Which is newer?
GPT-5 Chat is the newest, released Aug 7, 2025. Jamba Large came out Jul 3, 2025; o4-mini-deep-research came out Jun 26, 2025. Knowledge cutoff: GPT-5 Chat Sep 30, 2024, Jamba Large Aug 22, 2024, o4-mini-deep-research May 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 Nov 2, 2026.
