GLM-4.5 vs GLM-4.6
Too close to call on our weighted score (GLM-4.6 42, GLM-4.5 40). The right pick depends on what you value most.
Too close to call
It is close. Our weighted score puts them within 2 points (GLM-4.6 42/100, GLM-4.5 40/100), so choose by what matters most for your work: GLM-4.5 on price and GLM-4.6 for long inputs. 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 priceSame priceGLM-4.5 $1.00 · GLM-4.6 $1.00 per 1M tokens (3:1 blend)
- Longest contextGLM-4.6GLM-4.6 204,800 · GLM-4.5 131,072 tokens
- Widest inputsSame inputsGLM-4.5: Text · GLM-4.6: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.5 | GLM-4.6 |
|---|---|---|---|
| Price | 50% | 50 | 50 |
| Inputs & features | 30% | 35 | 35 |
| Context window | 20% | 24 | 32 |
| Overall | 100% | 40/100 | 42/100 |
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-4.5Z.ai (Zhipu) | GLM-4.6Z.ai (Zhipu) |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $0.60 | $0.60 |
| Output | $2.20 | $2.20 |
| Cached input | $0.11 | $0.11 |
| Blended (3:1) | $1.00 | $1.00 |
| Long-context rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Z.AI API |
| Limits | ||
| Context window | 131,072 tokens | 204,800 tokens (best) |
| Max output | 98,304 tokens | 131,072 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes | Yes |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Open | Open |
| API model ID | glm-4.5 | glm-4.6 |
| API providers | 14 | 18 (best) |
| Released | Jul 28, 2025 | Sep 30, 2025 |
| Knowledge cutoff | Apr 2025 | Apr 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-4.5$10.40
- GLM-4.6$10.40
Which should you choose?
Which is better: GLM-4.5 or GLM-4.6?
It is close. Our weighted score puts them within 2 points (GLM-4.6 42/100, GLM-4.5 40/100), so choose by what matters most for your work: GLM-4.5 on price and GLM-4.6 for long inputs. 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-4.5 or GLM-4.6?
GLM-4.5 and GLM-4.6 cost the same: $0.60 input / $2.20 output per million tokens.
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. GLM-4.5 has not been scored yet and GLM-4.6 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.5 and GLM-4.6 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.
Which has the bigger context window?
GLM-4.6 has the largest context window at 204,800 tokens, against 131,072 for GLM-4.5. Maximum output per response: GLM-4.5 up to 98,304, GLM-4.6 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5 accepts text; GLM-4.6 accepts text. They handle the same number of input types.
Are any of these open source?
Yes, both publish their weights, so you can self-host them.
Which is newer?
GLM-4.6 is the newest, released Sep 30, 2025. GLM-4.5 came out Jul 28, 2025. Knowledge cutoff: GLM-4.5 Apr 2025, GLM-4.6 Apr 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.
