DeepSeek V4 Pro vs Trinity Large Thinking vs GLM-5V-Turbo
Trinity Large Thinking comes out ahead, 55 to 49 and 45 on our weighted score, and it is the cheaper option too.
DeepSeekDeepSeek V4 Pro
45/100- ECI—
- Price$1.32 / $3.00
- Context1M
- Our pick
Arcee AITrinity Large Thinking
55/100- ECI—
- Price$0.25 / $0.80
- Context524K
Z.ai (Zhipu)GLM-5V-Turbo
49/100- ECI—
- Price$1.20 / $4.00
- Context200K
Trinity Large Thinking is our pick
Trinity Large Thinking is the better all-round choice, scoring 55/100 against GLM-5V-Turbo (49) and DeepSeek V4 Pro (45). It leads on price. DeepSeek V4 Pro wins on context window. GLM-5V-Turbo wins on inputs & features. 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 priceTrinity Large ThinkingTrinity Large Thinking $0.388 · DeepSeek V4 Pro $1.74 · GLM-5V-Turbo $1.90 per 1M tokens (3:1 blend)
- Longest contextDeepSeek V4 ProDeepSeek V4 Pro 1,000,000 · Trinity Large Thinking 524,288 · GLM-5V-Turbo 200,000 tokens
- Widest inputsGLM-5V-TurboDeepSeek V4 Pro: Text · Trinity Large Thinking: Text · GLM-5V-Turbo: Text, Images, PDFs, Video
- Self-hostingDeepSeek V4 Pro and Trinity Large ThinkingPublishes downloadable weights (OpenMDW-1.1)
| Measure | Weight | DeepSeek V4 Pro | Trinity Large Thinking | GLM-5V-Turbo |
|---|---|---|---|---|
| Price | 50% | 38 | 69 | 37 |
| Inputs & features | 30% | 45 | 35 | 80 |
| Context window | 20% | 60 | 49 | 32 |
| Overall | 100% | 45/100 | 55/100 | 49/100 |
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 ProDeepSeek | Trinity Large ThinkingArcee AI | GLM-5V-TurboZ.ai (Zhipu) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $1.32 | $0.25 (best) | $1.20 |
| Output | $3.00 | $0.80 (best) | $4.00 |
| Cached input | — | $0.06 (best) | $0.24 |
| Blended (3:1) | $1.74 | $0.388 (best) | $1.90 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 49 providers | Official Arcee API | Official Z.AI API |
| Limits | |||
| Context window | 1,000,000 tokens (best) | 524,288 tokens | 200,000 tokens |
| Max output | 384,000 tokens (best) | 262,144 tokens | 131,072 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | Yes |
| Audio | No | No | No |
| Video | No | No | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Open | OpenOpenMDW-1.1 | Proprietary |
| API model ID | — | trinity-large-thinking | glm-5v-turbo |
| API providers | 52 (best) | 6 | 14 |
| Released | Apr 24, 2026 | Apr 1, 2026 | Apr 1, 2026 |
| Knowledge cutoff | May 2025 | — | — |
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 Pro$19.20
- Trinity Large Thinking$4.10
- GLM-5V-Turbo$20.00
Which should you choose?
Which is better: DeepSeek V4 Pro, Trinity Large Thinking or GLM-5V-Turbo?
Trinity Large Thinking is the better all-round choice, scoring 55/100 against GLM-5V-Turbo (49) and DeepSeek V4 Pro (45). It leads on price. DeepSeek V4 Pro wins on context window. GLM-5V-Turbo wins on inputs & features. 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 Pro, Trinity Large Thinking or GLM-5V-Turbo?
Trinity Large Thinking is cheaper at $0.25 input / $0.80 output per million tokens (official Arcee API price). DeepSeek V4 Pro costs $1.32 input / $3.00 output per million tokens (median across 49 API providers); GLM-5V-Turbo costs $1.20 input / $4.00 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $0.388 per million tokens for Trinity Large Thinking versus $1.74 for DeepSeek V4 Pro (4.5× as much) and $1.90 for GLM-5V-Turbo (4.9× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. DeepSeek V4 Pro has not been scored yet, Trinity Large Thinking has not been scored yet and GLM-5V-Turbo has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek V4 Pro, Trinity Large Thinking and GLM-5V-Turbo 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 Pro has the largest context window at 1,000,000 tokens, against 524,288 for Trinity Large Thinking and 200,000 for GLM-5V-Turbo. Maximum output per response: DeepSeek V4 Pro up to 384,000, Trinity Large Thinking up to 262,144, GLM-5V-Turbo up to 131,072 tokens.
Which can read images, PDFs, audio or video?
DeepSeek V4 Pro accepts text; Trinity Large Thinking accepts text; GLM-5V-Turbo accepts text, images, PDFs and video. GLM-5V-Turbo handles the widest range of inputs.
Are any of these open source?
DeepSeek V4 Pro and Trinity Large Thinking publishes its weights (OpenMDW-1.1) and can be self-hosted; GLM-5V-Turbo is proprietary.
Which is newer?
DeepSeek V4 Pro is the newest, released Apr 24, 2026. Trinity Large Thinking came out Apr 1, 2026; GLM-5V-Turbo came out Apr 1, 2026. Knowledge cutoff: DeepSeek V4 Pro May 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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