Sarvam 105B vs Mercury 2
Too close to call on our weighted score (Sarvam 105B 52, Mercury 2 52). The right pick depends on what you value most.
Sarvam AI
Sarvam 105B
52/100- ECI—
- Price$0.158 / $0.632
- Context131K
InceptionMercury 2
52/100- ECI—
- Price$0.25 / $0.75
- Context128K
Add a model
Make it a three-way comparison.
Too close to call
It is close. Our weighted score puts them within a point (Sarvam 105B 52/100, Mercury 2 52/100), so choose by what matters most for your work: Sarvam 105B on price and Sarvam 105B for long inputs. 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 priceSarvam 105BSarvam 105B $0.276 · Mercury 2 $0.375 per 1M tokens (3:1 blend)
- Longest contextSarvam 105BSarvam 105B 131,072 · Mercury 2 128,000 tokens
- Widest inputsSame inputsSarvam 105B: Text · Mercury 2: Text
- Self-hostingSarvam 105BPublishes downloadable weights
| Measure | Weight | Sarvam 105B | Mercury 2 |
|---|---|---|---|
| Price | 50% | 73 | 67 |
| Inputs & features | 30% | 35 | 45 |
| Context window | 20% | 25 | 24 |
| Overall | 100% | 52/100 | 52/100 |
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 | Sarvam 105BSarvam AI | Mercury 2Inception |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $0.158 (best) | $0.25 |
| Output | $0.632 (best) | $0.75 |
| Cached input | $0.053 | $0.025 (best) |
| Blended (3:1) | $0.276 (best) | $0.375 |
| Long-context rate | Same rate | Same rate |
| Price source | Official Sarvam AI API | Official Inception API |
| Limits | ||
| Context window | 131,072 tokens (best) | 128,000 tokens |
| Max output | 131,072 tokens (best) | 50,000 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yeslow · medium · high | Yeslow · medium · high |
| Tool calling | Yes | Yes |
| Structured output | No | Yes |
| Availability | ||
| Weights | Open | Proprietary |
| API model ID | sarvam-105b | mercury-2 |
| API providers | 3 | 7 (best) |
| Released | Feb 18, 2026 | Feb 24, 2026 |
| Knowledge cutoff | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Sarvam 105B$2.84
- Mercury 2$4.00
Which should you choose?
Which is better: Sarvam 105B or Mercury 2?
It is close. Our weighted score puts them within a point (Sarvam 105B 52/100, Mercury 2 52/100), so choose by what matters most for your work: Sarvam 105B on price and Sarvam 105B for long inputs. 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, Sarvam 105B or Mercury 2?
Sarvam 105B is cheaper at $0.158 input / $0.632 output per million tokens (official Sarvam AI API price). Mercury 2 costs $0.25 input / $0.75 output per million tokens (official Inception API price). At a typical mix of three input tokens to one output token, that is $0.276 per million tokens for Sarvam 105B versus $0.375 for Mercury 2 (1.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Sarvam 105B has not been scored yet and Mercury 2 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Sarvam 105B and Mercury 2 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both support tool calling for agent workflows.
Which has the bigger context window?
Sarvam 105B has the largest context window at 131,072 tokens, against 128,000 for Mercury 2. Maximum output per response: Sarvam 105B up to 131,072, Mercury 2 up to 50,000 tokens.
Which can read images, PDFs, audio or video?
Sarvam 105B accepts text; Mercury 2 accepts text. They handle the same number of input types.
Are any of these open source?
Sarvam 105B publishes its weights and can be self-hosted; Mercury 2 is proprietary.
Which is newer?
Mercury 2 is the newest, released Feb 24, 2026. Sarvam 105B came out Feb 18, 2026.
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.
