o4-mini-deep-research vs Qwen3 235B-A22B Thinking 2507 vs Jamba Large
Qwen3 235B-A22B Thinking 2507 comes out ahead, 42 to 37 and 29 on our weighted score, and it is the cheaper option too.
OpenAIo4-mini-deep-research
37/100- ECI—
- Price$1.80 / $7.20
- Context200K
- Our pick
Alibaba (Qwen)Qwen3 235B-A22B Thinking 2507
42/100- ECI143.8
- Price$0.30 / $2.90
- Context262K
AI21 LabsJamba Large
29/100- ECI—
- Price$2.00 / $8.00
- Context256K
Qwen3 235B-A22B Thinking 2507 is our pick
Qwen3 235B-A22B Thinking 2507 is the better all-round choice, scoring 42/100 against o4-mini-deep-research (37) and Jamba Large (29). It leads on price. o4-mini-deep-research wins on inputs & features. 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 priceQwen3 235B-A22B Thinking 2507Qwen3 235B-A22B Thinking 2507 $0.95 · o4-mini-deep-research $3.15 · Jamba Large $3.50 per 1M tokens (3:1 blend)
- Longest contextQwen3 235B-A22B Thinking 2507Qwen3 235B-A22B Thinking 2507 262,144 · Jamba Large 256,000 · o4-mini-deep-research 200,000 tokens
- Widest inputso4-mini-deep-researcho4-mini-deep-research: Text, Images · Qwen3 235B-A22B Thinking 2507: Text · Jamba Large: Text
- Self-hostingQwen3 235B-A22B Thinking 2507 and Jamba LargePublishes downloadable weights (Apache 2.0)
| Measure | Weight | o4-mini-deep-research | Qwen3 235B-A22B Thinking 2507 | Jamba Large |
|---|---|---|---|---|
| Price | 50% | 25 | 49 | 23 |
| Inputs & features | 30% | 60 | 35 | 35 |
| Context window | 20% | 32 | 37 | 36 |
| Overall | 100% | 37/100 | 42/100 | 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 | Qwen3 235B-A22B Thinking 2507Alibaba (Qwen) | Jamba LargeAI21 Labs |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 143.8 | — |
| ECI rank | — | #93 of 165 | — |
| GPQA DiamondGraduate-level science questions | — | 80.1% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 86.7% | — |
| SimpleQA VerifiedShort factual questions | — | 40.4% | — |
| Price per million tokens | |||
| Input | $1.80 | $0.30 (best) | $2.00 |
| Output | $7.20 | $2.90 (best) | $8.00 |
| Cached input | — | — | — |
| Blended (3:1) | $3.15 | $0.95 (best) | $3.50 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Median of 11 providers | Official AI21 Labs API |
| Limits | |||
| Context window | 200,000 tokens | 262,144 tokens (best) | 256,000 tokens |
| Max output | 100,000 tokens (best) | 81,920 tokens | 4,096 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Proprietary | OpenApache 2.0 | Open |
| API model ID | — | — | jamba-large |
| API providers | 1 | 12 (best) | 1 |
| Released | Jun 26, 2025 | Jul 25, 2025 | Jul 3, 2025 |
| Knowledge cutoff | May 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
- Qwen3 235B-A22B Thinking 2507$8.80
- Jamba Large$36.00
Which should you choose?
Which is better: o4-mini-deep-research, Qwen3 235B-A22B Thinking 2507 or Jamba Large?
Qwen3 235B-A22B Thinking 2507 is the better all-round choice, scoring 42/100 against o4-mini-deep-research (37) and Jamba Large (29). It leads on price. o4-mini-deep-research wins on inputs & features. 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, Qwen3 235B-A22B Thinking 2507 or Jamba Large?
Qwen3 235B-A22B Thinking 2507 is cheaper at $0.30 input / $2.90 output per million tokens (median across 11 API providers). o4-mini-deep-research costs $1.80 input / $7.20 output per million tokens (median across 1 API provider); 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 $0.95 per million tokens for Qwen3 235B-A22B Thinking 2507 versus $3.15 for o4-mini-deep-research (3.3× as much) and $3.50 for Jamba Large (3.7× 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, Qwen3 235B-A22B Thinking 2507 has an ECI of 143.8 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, Qwen3 235B-A22B Thinking 2507 and Jamba Large 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?
Qwen3 235B-A22B Thinking 2507 has the largest context window at 262,144 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, Qwen3 235B-A22B Thinking 2507 up to 81,920, Jamba Large up to 4,096 tokens.
Which can read images, PDFs, audio or video?
o4-mini-deep-research accepts text and images; Qwen3 235B-A22B Thinking 2507 accepts text; Jamba Large accepts text. o4-mini-deep-research handles the widest range of inputs.
Are any of these open source?
Qwen3 235B-A22B Thinking 2507 and Jamba Large publishes its weights (Apache 2.0) and can be self-hosted; o4-mini-deep-research is proprietary.
Which is newer?
Qwen3 235B-A22B Thinking 2507 is the newest, released Jul 25, 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, 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.
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