Apertus 8B vs GLM-4.7-FlashX vs Granite-4.0-H-Small
Too close to call on our weighted score (Granite-4.0-H-Small 63, GLM-4.7-FlashX 61, Apertus 8B 56). The right pick depends on what you value most.
Swiss AI
Apertus 8B
56/100- ECI—
- Price$0.10 / $0.20
- Context66K
Z.ai (Zhipu)GLM-4.7-FlashX
61/100- ECI—
- Price$0.07 / $0.40
- Context200K
IBMGranite-4.0-H-Small
63/100- ECI—
- Price$0.064 / $0.265
- Context131K
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, Apertus 8B 56/100), 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 · Apertus 8B $0.125 · 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 · Apertus 8B 65,536 tokens
- Widest inputsSame inputsApertus 8B: Text · GLM-4.7-FlashX: Text · Granite-4.0-H-Small: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Apertus 8B | GLM-4.7-FlashX | Granite-4.0-H-Small |
|---|---|---|---|---|
| Price | 50% | 93 | 89 | 95 |
| Inputs & features | 30% | 25 | 35 | 35 |
| Context window | 20% | 12 | 32 | 24 |
| Overall | 100% | 56/100 | 61/100 | 63/100 |
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 | Apertus 8BSwiss AI | GLM-4.7-FlashXZ.ai (Zhipu) | Granite-4.0-H-SmallIBM |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.10 | $0.07 | $0.064 (best) |
| Output | $0.20 (best) | $0.40 | $0.265 |
| Cached input | — | $0.01 | — |
| Blended (3:1) | $0.125 | $0.152 | $0.114 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official Z.AI API | Official watsonx.ai API |
| Limits | |||
| Context window | 65,536 tokens | 200,000 tokens (best) | 131,072 tokens |
| Max output | 8,192 tokens | 131,072 tokens (best) | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | OpenApache-2.0 | Open | Open |
| API model ID | — | glm-4.7-flashx | ibm/granite-4-h-small |
| API providers | 1 | 8 (best) | 1 |
| Released | Sep 2, 2025 | Jan 19, 2026 | Oct 2, 2025 |
| Knowledge cutoff | Sep 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.
- Apertus 8B$1.40
- GLM-4.7-FlashX$1.50
- Granite-4.0-H-Small$1.17
Which should you choose?
Which is better: Apertus 8B, GLM-4.7-FlashX or Granite-4.0-H-Small?
It is close. Our weighted score puts them within 2 points (Granite-4.0-H-Small 63/100, GLM-4.7-FlashX 61/100, Apertus 8B 56/100), 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, Apertus 8B, GLM-4.7-FlashX or Granite-4.0-H-Small?
Granite-4.0-H-Small is cheaper at $0.064 input / $0.265 output per million tokens (official watsonx.ai API price). Apertus 8B costs $0.10 input / $0.20 output per million tokens (median across 1 API provider); 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 $0.125 for Apertus 8B (1.1× as much) and $0.152 for GLM-4.7-FlashX (1.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Apertus 8B has not been scored yet, GLM-4.7-FlashX has not been scored yet and Granite-4.0-H-Small has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Apertus 8B, GLM-4.7-FlashX and Granite-4.0-H-Small 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?
GLM-4.7-FlashX has the largest context window at 200,000 tokens, against 131,072 for Granite-4.0-H-Small and 65,536 for Apertus 8B. Maximum output per response: Apertus 8B up to 8,192, GLM-4.7-FlashX up to 131,072, Granite-4.0-H-Small up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Apertus 8B accepts text; GLM-4.7-FlashX accepts text; Granite-4.0-H-Small accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (Apache-2.0), 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; Apertus 8B came out Sep 2, 2025. Knowledge cutoff: Apertus 8B Sep 2025, 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.
