o4-mini-deep-research vs Voxtral Small 24B 2507 vs Jamba Large
Voxtral Small 24B 2507 comes out ahead, 55 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
Mistral AIVoxtral Small 24B 2507
55/100- ECI—
- Price$0.10 / $0.40
- Context33K
AI21 LabsJamba Large
29/100- ECI—
- Price$2.00 / $8.00
- Context256K
Voxtral Small 24B 2507 is our pick
Voxtral Small 24B 2507 is the better all-round choice, scoring 55/100 against o4-mini-deep-research (37) and Jamba Large (29). It leads on price. o4-mini-deep-research wins on inputs & features. 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 priceVoxtral Small 24B 2507Voxtral Small 24B 2507 $0.175 · 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 · Voxtral Small 24B 2507 32,768 tokens
- Widest inputso4-mini-deep-research and Voxtral Small 24B 2507o4-mini-deep-research: Text, Images · Voxtral Small 24B 2507: Text, Audio · Jamba Large: Text
- Self-hostingVoxtral Small 24B 2507 and Jamba LargePublishes downloadable weights (Apache 2.0)
| Measure | Weight | o4-mini-deep-research | Voxtral Small 24B 2507 | Jamba Large |
|---|---|---|---|---|
| Price | 50% | 25 | 82 | 23 |
| Inputs & features | 30% | 60 | 45 | 35 |
| Context window | 20% | 32 | 0 | 36 |
| Overall | 100% | 37/100 | 55/100 | 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 | Voxtral Small 24B 2507Mistral AI | Jamba LargeAI21 Labs |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $1.80 | $0.10 (best) | $2.00 |
| Output | $7.20 | $0.40 (best) | $8.00 |
| Cached input | — | — | — |
| Blended (3:1) | $3.15 | $0.175 (best) | $3.50 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official Mistral API | Official AI21 Labs API |
| Limits | |||
| Context window | 200,000 tokens | 32,768 tokens | 256,000 tokens (best) |
| Max output | 100,000 tokens (best) | 32,768 tokens | 4,096 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | Yes | No |
| Video | No | No | No |
| Reasoning | Yes | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | Proprietary | OpenApache 2.0 | Open |
| API model ID | — | voxtral-small-latest | jamba-large |
| API providers | 1 | 10 (best) | 1 |
| Released | Jun 26, 2025 | Jul 15, 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
- Voxtral Small 24B 2507$1.80
- Jamba Large$36.00
Which should you choose?
Which is better: o4-mini-deep-research, Voxtral Small 24B 2507 or Jamba Large?
Voxtral Small 24B 2507 is the better all-round choice, scoring 55/100 against o4-mini-deep-research (37) and Jamba Large (29). It leads on price. o4-mini-deep-research wins on inputs & features. 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, o4-mini-deep-research, Voxtral Small 24B 2507 or Jamba Large?
Voxtral Small 24B 2507 is cheaper at $0.10 input / $0.40 output per million tokens (official Mistral API price). 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 $0.175 per million tokens for Voxtral Small 24B 2507 versus $3.15 for o4-mini-deep-research (18× as much) and $3.50 for Jamba Large (20× 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, Voxtral Small 24B 2507 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, Voxtral Small 24B 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?
Jamba Large has the largest context window at 256,000 tokens, against 200,000 for o4-mini-deep-research and 32,768 for Voxtral Small 24B 2507. Maximum output per response: o4-mini-deep-research up to 100,000, Voxtral Small 24B 2507 up to 32,768, Jamba Large up to 4,096 tokens.
Which can read images, PDFs, audio or video?
o4-mini-deep-research accepts text and images; Voxtral Small 24B 2507 accepts text and audio; Jamba Large accepts text. o4-mini-deep-research handles the widest range of inputs.
Are any of these open source?
Voxtral Small 24B 2507 and Jamba Large publishes its weights (Apache 2.0) and can be self-hosted; o4-mini-deep-research is proprietary.
Which is newer?
Voxtral Small 24B 2507 is the newest, released Jul 15, 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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