Granite-4.0-H-Small vs GLM-4.7-FlashX
Too close to call on our weighted score (Granite-4.0-H-Small 63, GLM-4.7-FlashX 61). The right pick depends on what you value most.
IBMGranite-4.0-H-Small
63/100- ECI—
- Price$0.064 / $0.265
- Context131K
Z.ai (Zhipu)GLM-4.7-FlashX
61/100- ECI—
- Price$0.07 / $0.40
- Context200K
Add a model
Make it a three-way comparison.
Too close to call
It is close. Our weighted score puts them within 2 points (Granite-4.0-H-Small 63/100, GLM-4.7-FlashX 61/100), so choose by what matters most for your work: Granite-4.0-H-Small on price and GLM-4.7-FlashX 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 priceGranite-4.0-H-SmallGranite-4.0-H-Small $0.114 · GLM-4.7-FlashX $0.152 per 1M tokens (3:1 blend)
- Longest contextGLM-4.7-FlashXGLM-4.7-FlashX 200,000 · Granite-4.0-H-Small 131,072 tokens
- Widest inputsSame inputsGranite-4.0-H-Small: Text · GLM-4.7-FlashX: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Granite-4.0-H-Small | GLM-4.7-FlashX |
|---|---|---|---|
| Price | 50% | 95 | 89 |
| Inputs & features | 30% | 35 | 35 |
| Context window | 20% | 24 | 32 |
| Overall | 100% | 63/100 | 61/100 |
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 | Granite-4.0-H-SmallIBM | GLM-4.7-FlashXZ.ai (Zhipu) |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $0.064 (best) | $0.07 |
| Output | $0.265 (best) | $0.40 |
| Cached input | — | $0.01 |
| Blended (3:1) | $0.114 (best) | $0.152 |
| Long-context rate | Same rate | Same rate |
| Price source | Official watsonx.ai API | Official Z.AI API |
| Limits | ||
| Context window | 131,072 tokens | 200,000 tokens (best) |
| Max output | 131,072 tokens | 131,072 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | Yes |
| Tool calling | Yes | Yes |
| Structured output | Yes | No |
| Availability | ||
| Weights | Open | Open |
| API model ID | ibm/granite-4-h-small | glm-4.7-flashx |
| API providers | 1 | 8 (best) |
| Released | Oct 2, 2025 | Jan 19, 2026 |
| Knowledge cutoff | — | 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.
- Granite-4.0-H-Small$1.17
- GLM-4.7-FlashX$1.50
Which should you choose?
Which is better: Granite-4.0-H-Small or GLM-4.7-FlashX?
It is close. Our weighted score puts them within 2 points (Granite-4.0-H-Small 63/100, GLM-4.7-FlashX 61/100), so choose by what matters most for your work: Granite-4.0-H-Small on price and GLM-4.7-FlashX 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, Granite-4.0-H-Small or GLM-4.7-FlashX?
Granite-4.0-H-Small is cheaper at $0.064 input / $0.265 output per million tokens (official watsonx.ai API price). GLM-4.7-FlashX costs $0.07 input / $0.40 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $0.114 per million tokens for Granite-4.0-H-Small versus $0.152 for GLM-4.7-FlashX (1.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Granite-4.0-H-Small has not been scored yet and GLM-4.7-FlashX has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Granite-4.0-H-Small and GLM-4.7-FlashX 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-4.7-FlashX has the largest context window at 200,000 tokens, against 131,072 for Granite-4.0-H-Small. Maximum output per response: Granite-4.0-H-Small up to 131,072, GLM-4.7-FlashX up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Granite-4.0-H-Small accepts text; GLM-4.7-FlashX 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-4.7-FlashX is the newest, released Jan 19, 2026. Granite-4.0-H-Small came out Oct 2, 2025. Knowledge cutoff: GLM-4.7-FlashX 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.
