Sarvam 105B vs Trinity Large Thinking
Sarvam 105B comes out ahead, 65 to 55 on our weighted score, and it is the cheaper option too.
- Our pick
Sarvam AI
Sarvam 105B
65/100- ECI—
- Price$0.047 / $0.186
- Context131K
Arcee AITrinity Large Thinking
55/100- ECI—
- Price$0.25 / $0.80
- Context524K
Add a model
Make it a three-way comparison.
Sarvam 105B is our pick
Sarvam 105B is the better all-round choice, scoring 65/100 against Trinity Large Thinking (55). It leads on price. Trinity Large Thinking wins on context window. 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 $0.082 · Trinity Large Thinking $0.388 per 1M tokens (3:1 blend)
- Longest contextTrinity Large ThinkingTrinity Large Thinking 524,288 · Sarvam 105B 131,072 tokens
- Widest inputsSame inputsSarvam 105B: Text · Trinity Large Thinking: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Sarvam 105B | Trinity Large Thinking |
|---|---|---|---|
| Price | 50% | 100 | 69 |
| Inputs & features | 30% | 35 | 35 |
| Context window | 20% | 24 | 49 |
| Overall | 100% | 65/100 | 55/100 |
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 | Trinity Large ThinkingArcee AI |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $0.047 (best) | $0.25 |
| Output | $0.186 (best) | $0.80 |
| Cached input | — | $0.06 |
| Blended (3:1) | $0.082 (best) | $0.388 |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 2 providers | Official Arcee API |
| Limits | ||
| Context window | 131,072 tokens | 524,288 tokens (best) |
| Max output | 131,072 tokens | 262,144 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes · low · medium · high | Yes |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Open | OpenOpenMDW-1.1 |
| API model ID | sarvam-105b | trinity-large-thinking |
| API providers | 3 | 6 (best) |
| Released | Sep 1, 2025 | Apr 1, 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$0.842
- Trinity Large Thinking$4.10
Which should you choose?
Which is better: Sarvam 105B or Trinity Large Thinking?
Sarvam 105B is the better all-round choice, scoring 65/100 against Trinity Large Thinking (55). It leads on price. Trinity Large Thinking wins on context window. 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 Trinity Large Thinking?
Sarvam 105B is cheaper at $0.047 input / $0.186 output per million tokens (median across 2 API providers). Trinity Large Thinking costs $0.25 input / $0.80 output per million tokens (official Arcee API price). At a typical mix of three input tokens to one output token, that is $0.082 per million tokens for Sarvam 105B versus $0.388 for Trinity Large Thinking (4.7× 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 Trinity Large Thinking has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Sarvam 105B and Trinity Large Thinking 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?
Trinity Large Thinking has the largest context window at 524,288 tokens, against 131,072 for Sarvam 105B. Maximum output per response: Sarvam 105B up to 131,072, Trinity Large Thinking up to 262,144 tokens.
Which can read images, PDFs, audio or video?
Sarvam 105B accepts text; Trinity Large Thinking accepts text. They handle the same number of input types.
Are any of these open source?
Yes, both publish their weights (OpenMDW-1.1), so you can self-host them.
Which is newer?
Trinity Large Thinking is the newest, released Apr 1, 2026. Sarvam 105B came out Sep 1, 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 Oct 4, 2026.
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