DeepSeek V4 Flash Vision Exp vs Trinity Large Thinking

DeepSeek V4 Flash Vision Exp is the better all-round choice, scoring 70/100 against Trinity Large Thinking (55). It leads on price, inputs & features and 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.

DeepSeek V4 Flash Vision Exp is cheaper at $0.216 input / $0.647 output per million tokens (median across 15 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 $0.323 per million tokens for DeepSeek V4 Flash Vision Exp versus $0.388 for Trinity Large Thinking (1.2× as much).

There is no independent benchmark that covers both models yet. DeepSeek V4 Flash Vision Exp has not been scored yet and Trinity Large Thinking has not been scored yet.

There are no published SWE-bench Verified results for DeepSeek V4 Flash Vision Exp 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.

DeepSeek V4 Flash Vision Exp has the largest context window at 1,000,000 tokens, against 524,288 for Trinity Large Thinking. Maximum output per response: DeepSeek V4 Flash Vision Exp up to 384,000, Trinity Large Thinking up to 262,144 tokens.

DeepSeek V4 Flash Vision Exp accepts text and images; Trinity Large Thinking accepts text. DeepSeek V4 Flash Vision Exp handles the widest range of inputs.

Yes, both publish their weights (MIT and OpenMDW-1.1), so you can self-host them.

DeepSeek V4 Flash Vision Exp is the newest, released Aug 21, 2026. Trinity Large Thinking came out Apr 1, 2026.

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.