Sarvam 105B vs Trinity Large Thinking vs Seed 1.6 Flash
Seed 1.6 Flash comes out ahead, 78 to 65 and 55 on our weighted score, and it is the cheaper option too.
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
- Our pick
ByteDance SeedSeed 1.6 Flash
78/100- ECI—
- Price$0.022 / $0.223
- Context256K
Seed 1.6 Flash is our pick
Seed 1.6 Flash is the better all-round choice, scoring 78/100 against Sarvam 105B (65) and Trinity Large Thinking (55). It leads on inputs & features. 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 priceSeed 1.6 FlashSeed 1.6 Flash $0.072 · Sarvam 105B $0.082 · Trinity Large Thinking $0.388 per 1M tokens (3:1 blend)
- Longest contextTrinity Large ThinkingTrinity Large Thinking 524,288 · Seed 1.6 Flash 256,000 · Sarvam 105B 131,072 tokens
- Widest inputsSeed 1.6 FlashSarvam 105B: Text · Trinity Large Thinking: Text · Seed 1.6 Flash: Text, Images
- Self-hostingSarvam 105B and Trinity Large ThinkingPublishes downloadable weights (OpenMDW-1.1)
| Measure | Weight | Sarvam 105B | Trinity Large Thinking | Seed 1.6 Flash |
|---|---|---|---|---|
| Price | 50% | 100 | 69 | 100 |
| Inputs & features | 30% | 35 | 35 | 70 |
| Context window | 20% | 24 | 49 | 36 |
| Overall | 100% | 65/100 | 55/100 | 78/100 |
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 | Seed 1.6 FlashByteDance Seed |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.047 | $0.25 | $0.022 (best) |
| Output | $0.186 (best) | $0.80 | $0.223 |
| Cached input | — | $0.06 | $0.0044 (best) |
| Blended (3:1) | $0.082 | $0.388 | $0.072 (best) |
| Long-context rate | Same rate | Same rate | Over 32K: $0.045 / $0.445 |
| Price source | Median of 2 providers | Official Arcee API | Official Volcengine Ark API |
| Limits | |||
| Context window | 131,072 tokens | 524,288 tokens (best) | 256,000 tokens |
| Max output | 131,072 tokens | 262,144 tokens (best) | 32,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes · low · medium · high | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Open | OpenOpenMDW-1.1 | Proprietary |
| API model ID | sarvam-105b | trinity-large-thinking | doubao-seed-1-6-flash-250828 |
| API providers | 3 | 6 (best) | 2 |
| Released | Sep 1, 2025 | Apr 1, 2026 | Aug 28, 2025 |
| 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
- Seed 1.6 Flash$0.668
Which should you choose?
Which is better: Sarvam 105B, Trinity Large Thinking or Seed 1.6 Flash?
Seed 1.6 Flash is the better all-round choice, scoring 78/100 against Sarvam 105B (65) and Trinity Large Thinking (55). It leads on inputs & features. 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, Trinity Large Thinking or Seed 1.6 Flash?
Seed 1.6 Flash is cheaper at $0.022 input / $0.223 output per million tokens (official Volcengine Ark API price). Sarvam 105B costs $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 $0.072 per million tokens for Seed 1.6 Flash versus $0.082 for Sarvam 105B (1.1× as much) and $0.388 for Trinity Large Thinking (5.4× 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, Trinity Large Thinking has not been scored yet and Seed 1.6 Flash has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Sarvam 105B, Trinity Large Thinking and Seed 1.6 Flash 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?
Trinity Large Thinking has the largest context window at 524,288 tokens, against 256,000 for Seed 1.6 Flash and 131,072 for Sarvam 105B. Maximum output per response: Sarvam 105B up to 131,072, Trinity Large Thinking up to 262,144, Seed 1.6 Flash up to 32,000 tokens.
Which can read images, PDFs, audio or video?
Sarvam 105B accepts text; Trinity Large Thinking accepts text; Seed 1.6 Flash accepts text and images. Seed 1.6 Flash handles the widest range of inputs.
Are any of these open source?
Sarvam 105B and Trinity Large Thinking publishes its weights (OpenMDW-1.1) and can be self-hosted; Seed 1.6 Flash is proprietary.
Which is newer?
Trinity Large Thinking is the newest, released Apr 1, 2026. Sarvam 105B came out Sep 1, 2025; Seed 1.6 Flash came out Aug 28, 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.
