Devstral Small 2 vs GLM-4.7-FlashX vs Granite-4.0-H-Small
Devstral Small 2 comes out ahead, 67 to 63 and 61 on our weighted score, though Granite-4.0-H-Small is 24% cheaper per token.
- Our pick
Mistral AIDevstral Small 2
67/100- ECI—
- Price$0.10 / $0.30
- Context262K
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
Devstral Small 2 is our pick
Devstral Small 2 is the better all-round choice, scoring 67/100 against Granite-4.0-H-Small (63) and GLM-4.7-FlashX (61). It leads on inputs & features and context window. Granite-4.0-H-Small wins on price. 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 · Devstral Small 2 $0.15 · GLM-4.7-FlashX $0.152 per 1M tokens (3:1 blend)
- Longest contextDevstral Small 2Devstral Small 2 262,144 · GLM-4.7-FlashX 200,000 · Granite-4.0-H-Small 131,072 tokens
- Widest inputsDevstral Small 2Devstral Small 2: Text, Images · 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 | Devstral Small 2 | GLM-4.7-FlashX | Granite-4.0-H-Small |
|---|---|---|---|---|
| Price | 50% | 89 | 89 | 95 |
| Inputs & features | 30% | 50 | 35 | 35 |
| Context window | 20% | 37 | 32 | 24 |
| Overall | 100% | 67/100 | 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 | Devstral Small 2Mistral 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 | $0.30 | $0.40 | $0.265 (best) |
| Cached input | — | $0.01 | — |
| Blended (3:1) | $0.15 | $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 | 262,144 tokens (best) | 200,000 tokens | 131,072 tokens |
| Max output | 262,144 tokens (best) | 131,072 tokens | 131,072 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | 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 | Dec 9, 2025 | Jan 19, 2026 | Oct 2, 2025 |
| Knowledge cutoff | Dec 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.
- Devstral Small 2$1.60
- GLM-4.7-FlashX$1.50
- Granite-4.0-H-Small$1.17
Which should you choose?
Which is better: Devstral Small 2, GLM-4.7-FlashX or Granite-4.0-H-Small?
Devstral Small 2 is the better all-round choice, scoring 67/100 against Granite-4.0-H-Small (63) and GLM-4.7-FlashX (61). It leads on inputs & features and context window. Granite-4.0-H-Small wins on price. 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, Devstral Small 2, 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). Devstral Small 2 costs $0.10 input / $0.30 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 $0.15 for Devstral Small 2 (1.3× 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. Devstral Small 2 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 Devstral Small 2, 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?
Devstral Small 2 has the largest context window at 262,144 tokens, against 200,000 for GLM-4.7-FlashX and 131,072 for Granite-4.0-H-Small. Maximum output per response: Devstral Small 2 up to 262,144, 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?
Devstral Small 2 accepts text and images; GLM-4.7-FlashX accepts text; Granite-4.0-H-Small accepts text. Devstral Small 2 handles the widest range of inputs.
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. Devstral Small 2 came out Dec 9, 2025; Granite-4.0-H-Small came out Oct 2, 2025. Knowledge cutoff: Devstral Small 2 Dec 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.
