Sarvam-M vs Jamba Large
Jamba Large comes out ahead, 35 to 6 on our weighted score, and it is the cheaper option too.
Sarvam AI
Sarvam-M
6/100- ECI—
- Price—
- Context33K
- Our pick
AI21 LabsJamba Large
35/100- ECI—
- Price$2.00 / $8.00
- Context256K
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Make it a three-way comparison.
Jamba Large is our pick
Jamba Large is the better all-round choice, scoring 35/100 against Sarvam-M (6). It leads on inputs & features and 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 priceJamba LargeJamba Large $3.50 per 1M tokens (3:1 blend) · Sarvam-M unpriced
- Longest contextJamba LargeJamba Large 256,000 · Sarvam-M 32,768 tokens
- Widest inputsSame inputsSarvam-M: Text · Jamba Large: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Sarvam-M | Jamba Large |
|---|---|---|---|
| Inputs & features | 60% | 10 | 35 |
| Context window | 40% | 0 | 36 |
| Overall | 100% | 6/100 | 35/100 |
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 | Sarvam-MSarvam AI | Jamba LargeAI21 Labs |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | — | $2.00 |
| Output | — | $8.00 |
| Cached input | — | — |
| Blended (3:1) | — | $3.50 |
| Long-context rate | — | Same rate |
| Price source | — | Official AI21 Labs API |
| Limits | ||
| Context window | 32,768 tokens | 256,000 tokens (best) |
| Max output | 32,768 tokens (best) | 4,096 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes | No |
| Tool calling | No | Yes |
| Structured output | No | Yes |
| Availability | ||
| Weights | OpenApache 2.0 | Open |
| API model ID | — | jamba-large |
| API providers | — | 1 |
| Released | May 20, 2025 | Jul 3, 2025 |
| Knowledge cutoff | — | 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.
- Sarvam-M—
- Jamba Large$36.00
Which should you choose?
Which is better: Sarvam-M or Jamba Large?
Jamba Large is the better all-round choice, scoring 35/100 against Sarvam-M (6). It leads on inputs & features and 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, Sarvam-M or Jamba Large?
Jamba Large is cheaper at $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 $3.50 per million tokens for Jamba Large versus . Sarvam-M has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Sarvam-M 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 Sarvam-M and Jamba Large 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 32,768 for Sarvam-M. Maximum output per response: Sarvam-M up to 32,768, Jamba Large up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Sarvam-M accepts text; Jamba Large accepts text. They handle the same number of input types.
Are any of these open source?
Yes, both publish their weights (Apache 2.0), so you can self-host them.
Which is newer?
Jamba Large is the newest, released Jul 3, 2025. Sarvam-M came out May 20, 2025. Knowledge cutoff: 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.
