Granite 4.2 8B vs Ling 3.0 Flash Fin
Too close to call on our weighted score (Granite 4.2 8B 64, Ling 3.0 Flash Fin 63). The right pick depends on what you value most.
IBMGranite 4.2 8B
64/100- ECI—
- Price$0.06 / $0.15
- Context131K
inclusionAILing 3.0 Flash Fin
63/100- ECI—
- Price$0.075 / $0.22
- Context262K
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Make it a three-way comparison.
Too close to call
It is close. Our weighted score puts them within a point (Granite 4.2 8B 64/100, Ling 3.0 Flash Fin 63/100), so choose by what matters most for your work: Granite 4.2 8B on price and Ling 3.0 Flash Fin 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 4.2 8BGranite 4.2 8B $0.083 · Ling 3.0 Flash Fin $0.111 per 1M tokens (3:1 blend)
- Longest contextLing 3.0 Flash FinLing 3.0 Flash Fin 262,144 · Granite 4.2 8B 131,072 tokens
- Widest inputsSame inputsGranite 4.2 8B: Text · Ling 3.0 Flash Fin: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Granite 4.2 8B | Ling 3.0 Flash Fin |
|---|---|---|---|
| Price | 50% | 97 | 91 |
| Inputs & features | 30% | 35 | 35 |
| Context window | 20% | 25 | 37 |
| Overall | 100% | 64/100 | 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 | Granite 4.2 8BIBM | Ling 3.0 Flash FininclusionAI |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $0.06 (best) | $0.075 |
| Output | $0.15 (best) | $0.22 |
| Cached input | — | — |
| Blended (3:1) | $0.083 (best) | $0.111 |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 7 providers | Median of 2 providers |
| Limits | ||
| Context window | 131,072 tokens | 262,144 tokens (best) |
| Max output | 117,964 tokens (best) | 32,768 tokens |
| 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 | OpenMIT |
| API model ID | — | — |
| API providers | 7 (best) | 3 |
| Released | Aug 25, 2026 | Aug 27, 2026 |
| 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.
- Granite 4.2 8B$0.90
- Ling 3.0 Flash Fin$1.19
Which should you choose?
Which is better: Granite 4.2 8B or Ling 3.0 Flash Fin?
It is close. Our weighted score puts them within a point (Granite 4.2 8B 64/100, Ling 3.0 Flash Fin 63/100), so choose by what matters most for your work: Granite 4.2 8B on price and Ling 3.0 Flash Fin 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 4.2 8B or Ling 3.0 Flash Fin?
Granite 4.2 8B is cheaper at $0.06 input / $0.15 output per million tokens (median across 7 API providers). Ling 3.0 Flash Fin costs $0.075 input / $0.22 output per million tokens (median across 2 API providers). At a typical mix of three input tokens to one output token, that is $0.083 per million tokens for Granite 4.2 8B versus $0.111 for Ling 3.0 Flash Fin (1.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Granite 4.2 8B has not been scored yet and Ling 3.0 Flash Fin has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Granite 4.2 8B and Ling 3.0 Flash Fin 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 Fin has the largest context window at 262,144 tokens, against 131,072 for Granite 4.2 8B. Maximum output per response: Granite 4.2 8B up to 117,964, Ling 3.0 Flash Fin up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Granite 4.2 8B accepts text; Ling 3.0 Flash Fin 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 Fin is the newest, released Aug 27, 2026. Granite 4.2 8B came out Aug 25, 2026.
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.
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