Granite 4.2 8B vs Qwen3.8 Flash vs Ling 3.0 Flash VL
Too close to call on our weighted score (Ling 3.0 Flash VL 76, Qwen3.8 Flash 74, Granite 4.2 8B 64). The right pick depends on what you value most.
IBMGranite 4.2 8B
64/100- ECI—
- Price$0.06 / $0.15
- Context131K
Alibaba (Qwen)Qwen3.8 Flash
74/100- ECI—
- Price$0.15 / $0.47
- Context1M
inclusionAILing 3.0 Flash VL
76/100- ECI—
- Price$0.06 / $0.18
- Context262K
Too close to call
It is close. Our weighted score puts them within 2 points (Ling 3.0 Flash VL 76/100, Qwen3.8 Flash 74/100, Granite 4.2 8B 64/100), so choose by what matters most for your work: Granite 4.2 8B on price and Qwen3.8 Flash 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 VL $0.09 · Qwen3.8 Flash $0.23 per 1M tokens (3:1 blend)
- Longest contextQwen3.8 FlashQwen3.8 Flash 1,000,000 · Ling 3.0 Flash VL 262,144 · Granite 4.2 8B 131,072 tokens
- Widest inputsQwen3.8 Flash and Ling 3.0 Flash VLGranite 4.2 8B: Text · Qwen3.8 Flash: Text, Images, Video · Ling 3.0 Flash VL: Text, Images, Video
- Self-hostingGranite 4.2 8B and Ling 3.0 Flash VLPublishes downloadable weights (MIT)
| Measure | Weight | Granite 4.2 8B | Qwen3.8 Flash | Ling 3.0 Flash VL |
|---|---|---|---|---|
| Price | 50% | 97 | 77 | 95 |
| Inputs & features | 30% | 35 | 80 | 70 |
| Context window | 20% | 25 | 60 | 37 |
| Overall | 100% | 64/100 | 74/100 | 76/100 |
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 | Qwen3.8 FlashAlibaba (Qwen) | Ling 3.0 Flash VLinclusionAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.06 (best) | $0.15 | $0.06 (best) |
| Output | $0.15 (best) | $0.47 | $0.18 |
| Cached input | — | $0.016 | — |
| Blended (3:1) | $0.083 (best) | $0.23 | $0.09 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 7 providers | Official Alibaba API | Median of 7 providers |
| Limits | |||
| Context window | 131,072 tokens | 1,000,000 tokens (best) | 262,144 tokens |
| Max output | 117,964 tokens | 131,072 tokens (best) | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | Yes |
| Reasoning | Yes | Yeslow · medium · xhigh | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Proprietary | OpenMIT |
| API model ID | — | qwen3.8-flash | — |
| API providers | 7 | 28 (best) | 7 |
| Released | Aug 25, 2026 | Aug 26, 2026 | Sep 10, 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
- Qwen3.8 Flash$2.44
- Ling 3.0 Flash VL$0.96
Which should you choose?
Which is better: Granite 4.2 8B, Qwen3.8 Flash or Ling 3.0 Flash VL?
It is close. Our weighted score puts them within 2 points (Ling 3.0 Flash VL 76/100, Qwen3.8 Flash 74/100, Granite 4.2 8B 64/100), so choose by what matters most for your work: Granite 4.2 8B on price and Qwen3.8 Flash 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, Qwen3.8 Flash or Ling 3.0 Flash VL?
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 VL costs $0.06 input / $0.18 output per million tokens (median across 7 API providers); Qwen3.8 Flash costs $0.15 input / $0.47 output per million tokens (official Alibaba API price). 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 $0.09 for Ling 3.0 Flash VL (1.1× as much) and $0.23 for Qwen3.8 Flash (2.8× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Granite 4.2 8B has not been scored yet, Qwen3.8 Flash has not been scored yet and Ling 3.0 Flash VL has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Granite 4.2 8B, Qwen3.8 Flash and Ling 3.0 Flash VL 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?
Qwen3.8 Flash has the largest context window at 1,000,000 tokens, against 262,144 for Ling 3.0 Flash VL and 131,072 for Granite 4.2 8B. Maximum output per response: Granite 4.2 8B up to 117,964, Qwen3.8 Flash up to 131,072, Ling 3.0 Flash VL up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Granite 4.2 8B accepts text; Qwen3.8 Flash accepts text, images and video; Ling 3.0 Flash VL accepts text, images and video. Qwen3.8 Flash handles the widest range of inputs.
Are any of these open source?
Granite 4.2 8B and Ling 3.0 Flash VL publishes its weights (MIT) and can be self-hosted; Qwen3.8 Flash is proprietary.
Which is newer?
Ling 3.0 Flash VL is the newest, released Sep 10, 2026. Qwen3.8 Flash came out Aug 26, 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.
