o4-mini-deep-research vs GPT-5 Chat vs Jamba Large
o4-mini-deep-research comes out ahead, 37 to 31 and 29 on our weighted score, and it is the cheaper option too.
- Our pick
OpenAIo4-mini-deep-research
37/100- ECI—
- Price$1.80 / $7.20
- Context200K
- OpenAI
GPT-5 Chat
31/100- ECI—
- Price$1.25 / $10.00
- Context400K
AI21 LabsJamba Large
29/100- ECI—
- Price$2.00 / $8.00
- Context256K
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 inputso4-mini-deep-research and GPT-5 Chato4-mini-deep-research: Text, Images · GPT-5 Chat: Text, Images · Jamba Large: Text
- Self-hostingJamba LargePublishes downloadable weights
| Measure | Weight | o4-mini-deep-research | GPT-5 Chat | Jamba Large |
|---|---|---|---|---|
| Price | 50% | 25 | 23 | 23 |
| Inputs & features | 30% | 60 | 35 | 35 |
| Context window | 20% | 32 | 44 | 36 |
| Overall | 100% | 37/100 | 31/100 | 29/100 |
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 | o4-mini-deep-researchOpenAI | GPT-5 ChatOpenAI | Jamba LargeAI21 Labs |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $1.80 | $1.25 (best) | $2.00 |
| Output | $7.20 (best) | $10.00 | $8.00 |
| Cached input | — | — | — |
| Blended (3:1) | $3.15 (best) | $3.44 | $3.50 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Median of 5 providers | Official AI21 Labs API |
| Limits | |||
| Context window | 200,000 tokens | 400,000 tokens (best) | 256,000 tokens |
| Max output | 100,000 tokens | 128,000 tokens (best) | 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 | Yes | No |
| Tool calling | Yes | No | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Proprietary | Proprietary | Open |
| API model ID | — | — | jamba-large |
| API providers | 1 | 5 (best) | 1 |
| Released | Jun 26, 2025 | Aug 7, 2025 | Jul 3, 2025 |
| Knowledge cutoff | May 2024 | 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.
- o4-mini-deep-research$32.40
- GPT-5 Chat$32.50
- Jamba Large$36.00
Which should you choose?
Which is better: o4-mini-deep-research, GPT-5 Chat or Jamba Large?
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, o4-mini-deep-research, GPT-5 Chat or Jamba Large?
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. o4-mini-deep-research has not been scored yet, GPT-5 Chat 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 o4-mini-deep-research, GPT-5 Chat 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 200,000 for o4-mini-deep-research. Maximum output per response: o4-mini-deep-research up to 100,000, GPT-5 Chat up to 128,000, Jamba Large up to 4,096 tokens.
Which can read images, PDFs, audio or video?
o4-mini-deep-research accepts text and images; GPT-5 Chat accepts text and images; Jamba Large accepts text. o4-mini-deep-research handles the widest range of inputs.
Are any of these open source?
Jamba Large publishes its weights and can be self-hosted; o4-mini-deep-research and GPT-5 Chat 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: o4-mini-deep-research May 2024, 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 Nov 2, 2026.
