GLM-4.6V vs Qwen3 Coder Next vs Trinity Large Thinking
GLM-4.6V comes out ahead, 59 to 55 and 51 on our weighted score, though Trinity Large Thinking is 14% cheaper per token.
- Our pick
Z.ai (Zhipu)GLM-4.6V
59/100- ECI—
- Price$0.30 / $0.90
- Context128K
Alibaba (Qwen)Qwen3 Coder Next
51/100- ECI—
- Price$0.20 / $1.20
- Context262K
Arcee AITrinity Large Thinking
55/100- ECI—
- Price$0.25 / $0.80
- Context524K
GLM-4.6V is our pick
GLM-4.6V is the better all-round choice, scoring 59/100 against Trinity Large Thinking (55) and Qwen3 Coder Next (51). It leads on inputs & features. Trinity Large Thinking wins on price 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 priceTrinity Large ThinkingTrinity Large Thinking $0.388 · GLM-4.6V $0.45 · Qwen3 Coder Next $0.45 per 1M tokens (3:1 blend)
- Longest contextTrinity Large ThinkingTrinity Large Thinking 524,288 · Qwen3 Coder Next 262,144 · GLM-4.6V 128,000 tokens
- Widest inputsGLM-4.6VGLM-4.6V: Text, Images, Video · Qwen3 Coder Next: Text · Trinity Large Thinking: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.6V | Qwen3 Coder Next | Trinity Large Thinking |
|---|---|---|---|---|
| Price | 50% | 66 | 66 | 69 |
| Inputs & features | 30% | 70 | 35 | 35 |
| Context window | 20% | 24 | 37 | 49 |
| Overall | 100% | 59/100 | 51/100 | 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 | GLM-4.6VZ.ai (Zhipu) | Qwen3 Coder NextAlibaba (Qwen) | Trinity Large ThinkingArcee AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.30 | $0.20 (best) | $0.25 |
| Output | $0.90 | $1.20 | $0.80 (best) |
| Cached input | — | — | $0.06 |
| Blended (3:1) | $0.45 | $0.45 | $0.388 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Median of 11 providers | Official Arcee API |
| Limits | |||
| Context window | 128,000 tokens | 262,144 tokens | 524,288 tokens (best) |
| Max output | 32,768 tokens | 65,536 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Open | OpenOpenMDW-1.1 |
| API model ID | glm-4.6v | — | trinity-large-thinking |
| API providers | 10 | 11 (best) | 6 |
| Released | Dec 8, 2025 | Feb 3, 2026 | Apr 1, 2026 |
| Knowledge cutoff | Apr 2025 | Sep 2025 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GLM-4.6V$4.80
- Qwen3 Coder Next$4.40
- Trinity Large Thinking$4.10
Which should you choose?
Which is better: GLM-4.6V, Qwen3 Coder Next or Trinity Large Thinking?
GLM-4.6V is the better all-round choice, scoring 59/100 against Trinity Large Thinking (55) and Qwen3 Coder Next (51). It leads on inputs & features. Trinity Large Thinking wins on price 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, GLM-4.6V, Qwen3 Coder Next or Trinity Large Thinking?
Trinity Large Thinking is cheaper at $0.25 input / $0.80 output per million tokens (official Arcee API price). GLM-4.6V costs $0.30 input / $0.90 output per million tokens (official Z.AI API price); Qwen3 Coder Next costs $0.20 input / $1.20 output per million tokens (median across 11 API providers). At a typical mix of three input tokens to one output token, that is $0.388 per million tokens for Trinity Large Thinking versus $0.45 for GLM-4.6V (1.2× as much) and $0.45 for Qwen3 Coder Next (1.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.6V has not been scored yet, Qwen3 Coder Next 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 GLM-4.6V, Qwen3 Coder Next 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?
Trinity Large Thinking has the largest context window at 524,288 tokens, against 262,144 for Qwen3 Coder Next and 128,000 for GLM-4.6V. Maximum output per response: GLM-4.6V up to 32,768, Qwen3 Coder Next up to 65,536, Trinity Large Thinking up to 262,144 tokens.
Which can read images, PDFs, audio or video?
GLM-4.6V accepts text, images and video; Qwen3 Coder Next accepts text; Trinity Large Thinking accepts text. GLM-4.6V handles the widest range of inputs.
Are any of these open source?
Yes, all three 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. Qwen3 Coder Next came out Feb 3, 2026; GLM-4.6V came out Dec 8, 2025. Knowledge cutoff: GLM-4.6V Apr 2025, Qwen3 Coder Next Sep 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.
