Jamba Large vs o4-mini-deep-research vs Qwen3 VL 235B A22B Thinking
Qwen3 VL 235B A22B Thinking comes out ahead, 44 to 37 and 29 on our weighted score, and it is the cheaper option too.
AI21 LabsJamba Large
29/100- ECI—
- Price$2.00 / $8.00
- Context256K
OpenAIo4-mini-deep-research
37/100- ECI—
- Price$1.80 / $7.20
- Context200K
- Our pick
Alibaba (Qwen)Qwen3 VL 235B A22B Thinking
44/100- ECI—
- Price$0.50 / $3.95
- Context131K
Qwen3 VL 235B A22B Thinking is our pick
Qwen3 VL 235B A22B Thinking is the better all-round choice, scoring 44/100 against o4-mini-deep-research (37) and Jamba Large (29). It leads on price. Jamba Large 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 priceQwen3 VL 235B A22B ThinkingQwen3 VL 235B A22B Thinking $1.36 · o4-mini-deep-research $3.15 · Jamba Large $3.50 per 1M tokens (3:1 blend)
- Longest contextJamba LargeJamba Large 256,000 · o4-mini-deep-research 200,000 · Qwen3 VL 235B A22B Thinking 131,072 tokens
- Widest inputso4-mini-deep-research and Qwen3 VL 235B A22B ThinkingJamba Large: Text · o4-mini-deep-research: Text, Images · Qwen3 VL 235B A22B Thinking: Text, Images
- Self-hostingJamba Large and Qwen3 VL 235B A22B ThinkingPublishes downloadable weights
| Measure | Weight | Jamba Large | o4-mini-deep-research | Qwen3 VL 235B A22B Thinking |
|---|---|---|---|---|
| Price | 50% | 23 | 25 | 42 |
| Inputs & features | 30% | 35 | 60 | 60 |
| Context window | 20% | 36 | 32 | 25 |
| Overall | 100% | 29/100 | 37/100 | 44/100 |
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 | Jamba LargeAI21 Labs | o4-mini-deep-researchOpenAI | Qwen3 VL 235B A22B ThinkingAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $2.00 | $1.80 | $0.50 (best) |
| Output | $8.00 | $7.20 | $3.95 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $3.50 | $3.15 | $1.36 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official AI21 Labs API | Median of 1 providers | Median of 5 providers |
| Limits | |||
| Context window | 256,000 tokens (best) | 200,000 tokens | 131,072 tokens |
| Max output | 4,096 tokens | 100,000 tokens (best) | 32,768 tokens |
| 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 | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | jamba-large | — | — |
| API providers | 1 | 1 | 5 (best) |
| Released | Jul 3, 2025 | Jun 26, 2025 | Sep 23, 2025 |
| Knowledge cutoff | Aug 22, 2024 | May 2024 | Mar 31, 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Jamba Large$36.00
- o4-mini-deep-research$32.40
- Qwen3 VL 235B A22B Thinking$12.90
Which should you choose?
Which is better: Jamba Large, o4-mini-deep-research or Qwen3 VL 235B A22B Thinking?
Qwen3 VL 235B A22B Thinking is the better all-round choice, scoring 44/100 against o4-mini-deep-research (37) and Jamba Large (29). It leads on price. Jamba Large 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, Jamba Large, o4-mini-deep-research or Qwen3 VL 235B A22B Thinking?
Qwen3 VL 235B A22B Thinking is cheaper at $0.50 input / $3.95 output per million tokens (median across 5 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 $1.36 per million tokens for Qwen3 VL 235B A22B Thinking versus $3.15 for o4-mini-deep-research (2.3× as much) and $3.50 for Jamba Large (2.6× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Jamba Large has not been scored yet, o4-mini-deep-research has not been scored yet and Qwen3 VL 235B A22B Thinking has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Jamba Large, o4-mini-deep-research and Qwen3 VL 235B A22B Thinking 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?
Jamba Large has the largest context window at 256,000 tokens, against 200,000 for o4-mini-deep-research and 131,072 for Qwen3 VL 235B A22B Thinking. Maximum output per response: Jamba Large up to 4,096, o4-mini-deep-research up to 100,000, Qwen3 VL 235B A22B Thinking up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Jamba Large accepts text; o4-mini-deep-research accepts text and images; Qwen3 VL 235B A22B Thinking accepts text and images. o4-mini-deep-research handles the widest range of inputs.
Are any of these open source?
Jamba Large and Qwen3 VL 235B A22B Thinking publishes its weights and can be self-hosted; o4-mini-deep-research is proprietary.
Which is newer?
Qwen3 VL 235B A22B Thinking is the newest, released Sep 23, 2025. Jamba Large came out Jul 3, 2025; o4-mini-deep-research came out Jun 26, 2025. Knowledge cutoff: Jamba Large Aug 22, 2024, o4-mini-deep-research May 2024, Qwen3 VL 235B A22B Thinking Mar 31, 2025.
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.
