Sarvam 105B vs Mercury 2 vs Nemotron 3 Super 120B A12B
Too close to call on our weighted score (Nemotron 3 Super 120B A12B 54, 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
NVIDIANemotron 3 Super 120B A12B
54/100- ECI—
- Price$0.15 / $0.65
- Context262K
Too close to call
It is close. Our weighted score puts them within 2 points (Nemotron 3 Super 120B A12B 54/100, Sarvam 105B 52/100, Mercury 2 52/100), so choose by what matters most for your work: Nemotron 3 Super 120B A12B on price and Nemotron 3 Super 120B A12B 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 priceNemotron 3 Super 120B A12BNemotron 3 Super 120B A12B $0.275 · Sarvam 105B $0.276 · Mercury 2 $0.375 per 1M tokens (3:1 blend)
- Longest contextNemotron 3 Super 120B A12BNemotron 3 Super 120B A12B 262,144 · Sarvam 105B 131,072 · Mercury 2 128,000 tokens
- Widest inputsSame inputsSarvam 105B: Text · Mercury 2: Text · Nemotron 3 Super 120B A12B: Text
- Self-hostingSarvam 105B and Nemotron 3 Super 120B A12BPublishes downloadable weights
| Measure | Weight | Sarvam 105B | Mercury 2 | Nemotron 3 Super 120B A12B |
|---|---|---|---|---|
| Price | 50% | 73 | 67 | 73 |
| Inputs & features | 30% | 35 | 45 | 35 |
| Context window | 20% | 25 | 24 | 37 |
| Overall | 100% | 52/100 | 52/100 | 54/100 |
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 | Nemotron 3 Super 120B A12BNVIDIA |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.158 | $0.25 | $0.15 (best) |
| Output | $0.632 (best) | $0.75 | $0.65 |
| Cached input | $0.053 | $0.025 (best) | — |
| Blended (3:1) | $0.276 | $0.375 | $0.275 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Sarvam AI API | Official Inception API | Median of 16 providers |
| Limits | |||
| Context window | 131,072 tokens | 128,000 tokens | 262,144 tokens (best) |
| Max output | 131,072 tokens | 50,000 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yeslow · medium · high | Yeslow · medium · high | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | sarvam-105b | mercury-2 | — |
| API providers | 3 | 7 | 18 (best) |
| Released | Feb 18, 2026 | Feb 24, 2026 | Mar 11, 2026 |
| Knowledge cutoff | — | — | Jun 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 105B$2.84
- Mercury 2$4.00
- Nemotron 3 Super 120B A12B$2.80
Which should you choose?
Which is better: Sarvam 105B, Mercury 2 or Nemotron 3 Super 120B A12B?
It is close. Our weighted score puts them within 2 points (Nemotron 3 Super 120B A12B 54/100, Sarvam 105B 52/100, Mercury 2 52/100), so choose by what matters most for your work: Nemotron 3 Super 120B A12B on price and Nemotron 3 Super 120B A12B 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, Mercury 2 or Nemotron 3 Super 120B A12B?
Nemotron 3 Super 120B A12B is cheaper at $0.15 input / $0.65 output per million tokens (median across 16 API providers). Sarvam 105B costs $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 $0.275 per million tokens for Nemotron 3 Super 120B A12B versus $0.276 for Sarvam 105B (1× as much) and $0.375 for Mercury 2 (1.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Sarvam 105B has not been scored yet, Mercury 2 has not been scored yet and Nemotron 3 Super 120B A12B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Sarvam 105B, Mercury 2 and Nemotron 3 Super 120B A12B 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?
Nemotron 3 Super 120B A12B has the largest context window at 262,144 tokens, against 131,072 for Sarvam 105B and 128,000 for Mercury 2. Maximum output per response: Sarvam 105B up to 131,072, Mercury 2 up to 50,000, Nemotron 3 Super 120B A12B up to 262,144 tokens.
Which can read images, PDFs, audio or video?
Sarvam 105B accepts text; Mercury 2 accepts text; Nemotron 3 Super 120B A12B accepts text. They handle the same number of input types.
Are any of these open source?
Sarvam 105B and Nemotron 3 Super 120B A12B publishes its weights and can be self-hosted; Mercury 2 is proprietary.
Which is newer?
Nemotron 3 Super 120B A12B is the newest, released Mar 11, 2026. Mercury 2 came out Feb 24, 2026; Sarvam 105B came out Feb 18, 2026. Knowledge cutoff: Nemotron 3 Super 120B A12B Jun 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.
