Ling 3.0 Flash vs Granite-4.0-H-Small
Ling 3.0 Flash comes out ahead, 65 to 61 on our weighted score, and it is the cheaper option too.
- Our pick
inclusionAILing 3.0 Flash
65/100- ECI—
- Price$0.06 / $0.18
- Context262K
IBMGranite-4.0-H-Small
61/100- ECI—
- Price$0.064 / $0.265
- Context131K
Add a model
Make it a three-way comparison.
Ling 3.0 Flash is our pick
Ling 3.0 Flash is the better all-round choice, scoring 65/100 against Granite-4.0-H-Small (61). It leads on price and 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 priceLing 3.0 FlashLing 3.0 Flash $0.09 · Granite-4.0-H-Small $0.114 per 1M tokens (3:1 blend)
- Longest contextLing 3.0 FlashLing 3.0 Flash 262,144 · Granite-4.0-H-Small 131,072 tokens
- Widest inputsSame inputsLing 3.0 Flash: Text · Granite-4.0-H-Small: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Ling 3.0 Flash | Granite-4.0-H-Small |
|---|---|---|---|
| Price | 50% | 95 | 91 |
| Inputs & features | 30% | 35 | 35 |
| Context window | 20% | 37 | 25 |
| Overall | 100% | 65/100 | 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 | Ling 3.0 FlashinclusionAI | Granite-4.0-H-SmallIBM |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $0.06 (best) | $0.064 |
| Output | $0.18 (best) | $0.265 |
| Cached input | — | — |
| Blended (3:1) | $0.09 (best) | $0.114 |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 7 providers | Official watsonx.ai API |
| Limits | ||
| Context window | 262,144 tokens (best) | 131,072 tokens |
| Max output | 32,768 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 | No |
| Tool calling | Yes | Yes |
| Structured output | No | Yes |
| Availability | ||
| Weights | OpenMIT | Open |
| API model ID | — | ibm/granite-4-h-small |
| API providers | 9 (best) | 1 |
| Released | Jul 23, 2026 | Oct 2, 2025 |
| Knowledge cutoff | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Ling 3.0 Flash$0.96
- Granite-4.0-H-Small$1.17
Which should you choose?
Which is better: Ling 3.0 Flash or Granite-4.0-H-Small?
Ling 3.0 Flash is the better all-round choice, scoring 65/100 against Granite-4.0-H-Small (61). It leads on price and 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, Ling 3.0 Flash or Granite-4.0-H-Small?
Ling 3.0 Flash is cheaper at $0.06 input / $0.18 output per million tokens (median across 7 API providers). Granite-4.0-H-Small costs $0.064 input / $0.265 output per million tokens (official watsonx.ai API price). At a typical mix of three input tokens to one output token, that is $0.09 per million tokens for Ling 3.0 Flash versus $0.114 for Granite-4.0-H-Small (1.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Ling 3.0 Flash 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 Ling 3.0 Flash 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. Both support tool calling for agent workflows.
Which has the bigger context window?
Ling 3.0 Flash has the largest context window at 262,144 tokens, against 131,072 for Granite-4.0-H-Small. Maximum output per response: Ling 3.0 Flash up to 32,768, Granite-4.0-H-Small up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Ling 3.0 Flash accepts text; Granite-4.0-H-Small accepts text. They handle the same number of input types.
Are any of these open source?
Yes, both publish their weights (MIT), so you can self-host them.
Which is newer?
Ling 3.0 Flash is the newest, released Jul 23, 2026. Granite-4.0-H-Small came out Oct 2, 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 Nov 2, 2026.
Related comparisons
- Add Nemotron 3.5 Lightning 30B A3BThree-way comparison
- Ling 3.0 Flash vs Nemotron 3.5 Lightning 30B A3BNVIDIA
- Ling 3.0 Flash vs Granite 4.2 8BIBM
- Ling 3.0 Flash vs Laguna S 2.1Poolside
- Ling 3.0 Flash vs DiffusionGemma 26B A4B ITGoogle
- Granite-4.0-H-Small vs Apertus 8BSwiss AI
- Granite-4.0-H-Small vs Nemotron Nano 9B v2NVIDIA
