Jamba Large vs o4-mini-deep-research vs Sarvam-M
o4-mini-deep-research comes out ahead, 49 to 35 and 6 on our weighted score, and it is the cheaper option too.
AI21 LabsJamba Large
35/100- ECI—
- Price$2.00 / $8.00
- Context256K
- Our pick
OpenAIo4-mini-deep-research
49/100- ECI—
- Price$1.80 / $7.20
- Context200K
Sarvam AI
Sarvam-M
6/100- ECI—
- Price—
- Context33K
o4-mini-deep-research is our pick
o4-mini-deep-research is the better all-round choice, scoring 49/100 against Jamba Large (35) and Sarvam-M (6). It leads on inputs & features. Jamba Large wins on context window. The score weighs inputs & features 60%, context window 40%. 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 · Jamba Large $3.50 per 1M tokens (3:1 blend) · Sarvam-M unpriced
- Longest contextJamba LargeJamba Large 256,000 · o4-mini-deep-research 200,000 · Sarvam-M 32,768 tokens
- Widest inputso4-mini-deep-researchJamba Large: Text · o4-mini-deep-research: Text, Images · Sarvam-M: Text
- Self-hostingJamba Large and Sarvam-MPublishes downloadable weights (Apache 2.0)
| Measure | Weight | Jamba Large | o4-mini-deep-research | Sarvam-M |
|---|---|---|---|---|
| Inputs & features | 60% | 35 | 60 | 10 |
| Context window | 40% | 36 | 32 | 0 |
| Overall | 100% | 35/100 | 49/100 | 6/100 |
Left out because at least one model lacks the data: capability and price. 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 | Sarvam-MSarvam AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $2.00 | $1.80 (best) | — |
| Output | $8.00 | $7.20 (best) | — |
| Cached input | — | — | — |
| Blended (3:1) | $3.50 | $3.15 (best) | — |
| Long-context rate | Same rate | Same rate | — |
| Price source | Official AI21 Labs API | Median of 1 providers | — |
| Limits | |||
| Context window | 256,000 tokens (best) | 200,000 tokens | 32,768 tokens |
| Max output | 4,096 tokens | 100,000 tokens (best) | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | Yes | Yes |
| Tool calling | Yes | Yes | No |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Open | Proprietary | OpenApache 2.0 |
| API model ID | jamba-large | — | — |
| API providers | 1 | 1 | — |
| Released | Jul 3, 2025 | Jun 26, 2025 | May 20, 2025 |
| Knowledge cutoff | 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.
- Jamba Large$36.00
- o4-mini-deep-research$32.40
- Sarvam-M—
Which should you choose?
Which is better: Jamba Large, o4-mini-deep-research or Sarvam-M?
o4-mini-deep-research is the better all-round choice, scoring 49/100 against Jamba Large (35) and Sarvam-M (6). It leads on inputs & features. Jamba Large wins on context window. The score weighs inputs & features 60%, context window 40%. 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 Sarvam-M?
o4-mini-deep-research is cheaper at $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 $3.15 per million tokens for o4-mini-deep-research versus $3.50 for Jamba Large (1.1× as much). Sarvam-M has no published per-token price.
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 Sarvam-M 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 Sarvam-M yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Sarvam-M does not support tool calling, which most coding agents need.
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 Sarvam-M. Maximum output per response: Jamba Large up to 4,096, o4-mini-deep-research up to 100,000, Sarvam-M 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; Sarvam-M accepts text. o4-mini-deep-research handles the widest range of inputs.
Are any of these open source?
Jamba Large and Sarvam-M publishes its weights (Apache 2.0) and can be self-hosted; o4-mini-deep-research is proprietary.
Which is newer?
Jamba Large is the newest, released Jul 3, 2025. o4-mini-deep-research came out Jun 26, 2025; Sarvam-M came out May 20, 2025. Knowledge cutoff: 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.
