GLM-4.6V vs GPT-5.4 nano vs Qwen3 Coder Next
GPT-5.4 nano comes out ahead, 63 to 59 and 51 on our weighted score.
Z.ai (Zhipu)GLM-4.6V
59/100- ECI—
- Price$0.30 / $0.90
- Context128K
- Our pick
OpenAIGPT-5.4 nano
63/100- ECI145.8
- Price$0.20 / $1.25
- Context400K
Alibaba (Qwen)Qwen3 Coder Next
51/100- ECI—
- Price$0.20 / $1.20
- Context262K
GPT-5.4 nano is our pick
GPT-5.4 nano is the better all-round choice, scoring 63/100 against GLM-4.6V (59) and Qwen3 Coder Next (51). It leads 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 priceGLM-4.6V and Qwen3 Coder NextGLM-4.6V $0.45 · Qwen3 Coder Next $0.45 · GPT-5.4 nano $0.463 per 1M tokens (3:1 blend)
- Longest contextGPT-5.4 nanoGPT-5.4 nano 400,000 · Qwen3 Coder Next 262,144 · GLM-4.6V 128,000 tokens
- Widest inputsGLM-4.6VGLM-4.6V: Text, Images, Video · GPT-5.4 nano: Text, Images · Qwen3 Coder Next: Text
- Self-hostingGLM-4.6V and Qwen3 Coder NextPublishes downloadable weights
| Measure | Weight | GLM-4.6V | GPT-5.4 nano | Qwen3 Coder Next |
|---|---|---|---|---|
| Price | 50% | 66 | 66 | 66 |
| Inputs & features | 30% | 70 | 70 | 35 |
| Context window | 20% | 24 | 44 | 37 |
| Overall | 100% | 59/100 | 63/100 | 51/100 |
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) | GPT-5.4 nanoOpenAI | Qwen3 Coder NextAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 145.8 | — |
| ECI rank | — | #75 of 148 | — |
| GPQA DiamondGraduate-level science questions | — | 78.5% | — |
| FrontierMath Tiers 1–3Research-level mathematics | — | 44.9% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 87.8% | — |
| SimpleQA VerifiedShort factual questions | — | 11.7% | — |
| Price per million tokens | |||
| Input | $0.30 | $0.20 (best) | $0.20 (best) |
| Output | $0.90 (best) | $1.25 | $1.20 |
| Cached input | — | $0.02 | — |
| Blended (3:1) | $0.45 (best) | $0.463 | $0.45 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official OpenAI API | Median of 11 providers |
| Limits | |||
| Context window | 128,000 tokens | 400,000 tokens (best) | 262,144 tokens |
| Max output | 32,768 tokens | 128,000 tokens (best) | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | Yeslow · medium · high · xhigh | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | glm-4.6v | gpt-5.4-nano | — |
| API providers | 10 | 26 (best) | 11 |
| Released | Dec 8, 2025 | Mar 17, 2026 | Feb 3, 2026 |
| Knowledge cutoff | Apr 2025 | Aug 31, 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
- GPT-5.4 nano$4.50
- Qwen3 Coder Next$4.40
Which should you choose?
Which is better: GLM-4.6V, GPT-5.4 nano or Qwen3 Coder Next?
GPT-5.4 nano is the better all-round choice, scoring 63/100 against GLM-4.6V (59) and Qwen3 Coder Next (51). It leads 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, GLM-4.6V, GPT-5.4 nano or Qwen3 Coder Next?
GLM-4.6V is cheaper at $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); GPT-5.4 nano costs $0.20 input / $1.25 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $0.45 per million tokens for GLM-4.6V versus $0.45 for Qwen3 Coder Next (1× as much) and $0.463 for GPT-5.4 nano (1× 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, GPT-5.4 nano has an ECI of 145.8 and Qwen3 Coder Next has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.6V, GPT-5.4 nano and Qwen3 Coder Next 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?
GPT-5.4 nano has the largest context window at 400,000 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, GPT-5.4 nano up to 128,000, Qwen3 Coder Next up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GLM-4.6V accepts text, images and video; GPT-5.4 nano accepts text and images; Qwen3 Coder Next accepts text. GLM-4.6V handles the widest range of inputs.
Are any of these open source?
GLM-4.6V and Qwen3 Coder Next publishes its weights and can be self-hosted; GPT-5.4 nano is proprietary.
Which is newer?
GPT-5.4 nano is the newest, released Mar 17, 2026. Qwen3 Coder Next came out Feb 3, 2026; GLM-4.6V came out Dec 8, 2025. Knowledge cutoff: GLM-4.6V Apr 2025, GPT-5.4 nano Aug 31, 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.
