Sarvam-M vs Jamba Large vs Solar Pro 2
Jamba Large comes out ahead, 35 to 26 and 6 on our weighted score, though Solar Pro 2 is 14× cheaper per token.
Sarvam AI
Sarvam-M
6/100- ECI—
- Price—
- Context33K
- Our pick
AI21 LabsJamba Large
35/100- ECI—
- Price$2.00 / $8.00
- Context256K
UpstageSolar Pro 2
26/100- ECI—
- Price$0.25 / $0.25
- Context66K
Jamba Large is our pick
Jamba Large is the better all-round choice, scoring 35/100 against Solar Pro 2 (26) and Sarvam-M (6). It leads 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 priceSolar Pro 2Solar Pro 2 $0.25 · Jamba Large $3.50 per 1M tokens (3:1 blend) · Sarvam-M unpriced
- Longest contextJamba LargeJamba Large 256,000 · Solar Pro 2 65,536 · Sarvam-M 32,768 tokens
- Widest inputsSame inputsSarvam-M: Text · Jamba Large: Text · Solar Pro 2: Text
- Self-hostingSarvam-M and Jamba LargePublishes downloadable weights (Apache 2.0)
| Measure | Weight | Sarvam-M | Jamba Large | Solar Pro 2 |
|---|---|---|---|---|
| Inputs & features | 60% | 10 | 35 | 35 |
| Context window | 40% | 0 | 36 | 12 |
| Overall | 100% | 6/100 | 35/100 | 26/100 |
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 | Solar Pro 2Upstage |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | — | $2.00 | $0.25 (best) |
| Output | — | $8.00 | $0.25 (best) |
| Cached input | — | — | — |
| Blended (3:1) | — | $3.50 | $0.25 (best) |
| Long-context rate | — | Same rate | Same rate |
| Price source | — | Official AI21 Labs API | Official Upstage API |
| Limits | |||
| Context window | 32,768 tokens | 256,000 tokens (best) | 65,536 tokens |
| Max output | 32,768 tokens (best) | 4,096 tokens | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | Yesminimal · high |
| Tool calling | No | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | OpenApache 2.0 | Open | Proprietary |
| API model ID | — | jamba-large | solar-pro2 |
| API providers | — | 1 | 2 (best) |
| Released | May 20, 2025 | Jul 3, 2025 | May 20, 2025 |
| Knowledge cutoff | — | Aug 22, 2024 | Mar 2025 |
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
- Solar Pro 2$3.00
Which should you choose?
Which is better: Sarvam-M, Jamba Large or Solar Pro 2?
Jamba Large is the better all-round choice, scoring 35/100 against Solar Pro 2 (26) and Sarvam-M (6). It leads 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, Sarvam-M, Jamba Large or Solar Pro 2?
Solar Pro 2 is cheaper at $0.25 input / $0.25 output per million tokens (official Upstage API price). 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 $0.25 per million tokens for Solar Pro 2 versus $3.50 for Jamba Large (14× 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. Sarvam-M has not been scored yet, Jamba Large has not been scored yet and Solar Pro 2 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Sarvam-M, Jamba Large and Solar Pro 2 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 65,536 for Solar Pro 2 and 32,768 for Sarvam-M. Maximum output per response: Sarvam-M up to 32,768, Jamba Large up to 4,096, Solar Pro 2 up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Sarvam-M accepts text; Jamba Large accepts text; Solar Pro 2 accepts text. They handle the same number of input types.
Are any of these open source?
Sarvam-M and Jamba Large publishes its weights (Apache 2.0) and can be self-hosted; Solar Pro 2 is proprietary.
Which is newer?
Jamba Large is the newest, released Jul 3, 2025. Sarvam-M came out May 20, 2025; Solar Pro 2 came out May 20, 2025. Knowledge cutoff: Jamba Large Aug 22, 2024, Solar Pro 2 Mar 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.
