DeepSeek V4 Flash Vision Exp vs Toast 1 vs Trinity Large Thinking
DeepSeek V4 Flash Vision Exp comes out ahead, 70 to 55 and 47 on our weighted score, and it is the cheaper option too.
- Our pick
DeepSeekDeepSeek V4 Flash Vision Exp
70/100- ECI—
- Price$0.216 / $0.647
- Context1M
Mixedbread
Toast 1
47/100- ECI—
- Price$0.30 / $0.72
- Context131K
Arcee AITrinity Large Thinking
55/100- ECI—
- Price$0.25 / $0.80
- Context524K
DeepSeek V4 Flash Vision Exp is our pick
DeepSeek V4 Flash Vision Exp is the better all-round choice, scoring 70/100 against Trinity Large Thinking (55) and Toast 1 (47). 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.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceDeepSeek V4 Flash Vision ExpDeepSeek V4 Flash Vision Exp $0.323 · Trinity Large Thinking $0.388 · Toast 1 $0.405 per 1M tokens (3:1 blend)
- Longest contextDeepSeek V4 Flash Vision ExpDeepSeek V4 Flash Vision Exp 1,000,000 · Trinity Large Thinking 524,288 · Toast 1 131,000 tokens
- Widest inputsDeepSeek V4 Flash Vision ExpDeepSeek V4 Flash Vision Exp: Text, Images · Toast 1: Text · Trinity Large Thinking: Text
- Self-hostingDeepSeek V4 Flash Vision Exp and Trinity Large ThinkingPublishes downloadable weights (MIT and OpenMDW-1.1)
| Measure | Weight | DeepSeek V4 Flash Vision Exp | Toast 1 | Trinity Large Thinking |
|---|---|---|---|---|
| Price | 50% | 73 | 68 | 69 |
| Inputs & features | 30% | 70 | 25 | 35 |
| Context window | 20% | 60 | 24 | 49 |
| Overall | 100% | 70/100 | 47/100 | 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 | DeepSeek V4 Flash Vision ExpDeepSeek | Toast 1Mixedbread | Trinity Large ThinkingArcee AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.216 (best) | $0.30 | $0.25 |
| Output | $0.647 (best) | $0.72 | $0.80 |
| Cached input | — | — | $0.06 |
| Blended (3:1) | $0.323 (best) | $0.405 | $0.388 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 15 providers | Median of 1 providers | Official Arcee API |
| Limits | |||
| Context window | 1,000,000 tokens (best) | 131,000 tokens | 524,288 tokens |
| Max output | 384,000 tokens (best) | 4,000 tokens | 262,144 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | OpenMIT | Proprietary | OpenOpenMDW-1.1 |
| API model ID | — | — | trinity-large-thinking |
| API providers | 15 (best) | 1 | 6 |
| Released | Aug 21, 2026 | Aug 13, 2026 | 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.
- DeepSeek V4 Flash Vision Exp$3.45
- Toast 1$4.44
- Trinity Large Thinking$4.10
Which should you choose?
Which is better: DeepSeek V4 Flash Vision Exp, Toast 1 or Trinity Large Thinking?
DeepSeek V4 Flash Vision Exp is the better all-round choice, scoring 70/100 against Trinity Large Thinking (55) and Toast 1 (47). 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.
Which is cheaper, DeepSeek V4 Flash Vision Exp, Toast 1 or Trinity Large Thinking?
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); Toast 1 costs $0.30 input / $0.72 output per million tokens (median across 1 API provider). 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) and $0.405 for Toast 1 (1.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. DeepSeek V4 Flash Vision Exp has not been scored yet, Toast 1 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 DeepSeek V4 Flash Vision Exp, Toast 1 and Trinity Large Thinking 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?
DeepSeek V4 Flash Vision Exp has the largest context window at 1,000,000 tokens, against 524,288 for Trinity Large Thinking and 131,000 for Toast 1. Maximum output per response: DeepSeek V4 Flash Vision Exp up to 384,000, Toast 1 up to 4,000, Trinity Large Thinking up to 262,144 tokens.
Which can read images, PDFs, audio or video?
DeepSeek V4 Flash Vision Exp accepts text and images; Toast 1 accepts text; Trinity Large Thinking accepts text. DeepSeek V4 Flash Vision Exp handles the widest range of inputs.
Are any of these open source?
DeepSeek V4 Flash Vision Exp and Trinity Large Thinking publishes its weights (MIT and OpenMDW-1.1) and can be self-hosted; Toast 1 is proprietary.
Which is newer?
DeepSeek V4 Flash Vision Exp is the newest, released Aug 21, 2026. Toast 1 came out Aug 13, 2026; Trinity Large Thinking came out Apr 1, 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 Oct 4, 2026.
