Coding
Directional only- Granite-4.0-H-1B
- 44.1
- Estimated · #116/183
- Ling 2.6 Flash
- 42.7
- Estimated · #127/183
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Directional only
Five or fewer confirmed AI changes, with original sources, on mornings when something changed.A free source-linked morning brief.
See the free Radar BriefUpdated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.
0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Prompts that approach the documented context limit
Ling 2.6 Flash
Ling 2.6 Flash has the larger documented context window.
Confidence: documented
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Granite-4.0-H-1B and Ling 2.6 Flash are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Granite-4.0-H-1B is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Granite-4.0-H-1B does not fit this workload in one request. Granite-4.0-H-1B has no comparable published API token rate. Ling 2.6 Flash has no comparable published API token rate.
Confidence: listed-rates
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
| Category | Granite-4.0-H-1B | Ling 2.6 Flash | Basis | Reading |
|---|---|---|---|---|
| Coding | 44.1Estimated · #116/183 | 42.7Estimated · #127/183 | Directional onlyBenchAlign lane · 0 vs 0 public rows | Directional only |
| Knowledge | 36.2Estimated · #157/181 | 41.4Estimated · #132/181 | Directional onlyBenchAlign lane · 0 vs 0 public rows | Directional only |
| Instruction following | 28.5#115/120 | 46.6#86/120 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Agentic | Not ranked | 39.9Estimated · #122/151 | Not comparableBenchAlign lane · 0 vs 0 public rows | Not comparable |
| Reasoning | 25.3Unranked · 2 rankable rows | 41.2Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
1K fresh input + 500 output tokens
Granite-4.0-H-1B has no comparable published API token rate. Ling 2.6 Flash has no comparable published API token rate.
50K fresh input + 3K output tokens
Granite-4.0-H-1B has no comparable published API token rate. Ling 2.6 Flash has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Granite-4.0-H-1B does not fit this workload in one request. Granite-4.0-H-1B has no comparable published API token rate. Ling 2.6 Flash has no comparable published API token rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Granite-4.0-H-1B
128K
Ling 2.6 Flash
262K
Granite-4.0-H-1B
Not sourced
Ling 2.6 Flash
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Granite-4.0-H-1B
No comparable hosted API rate
Ling 2.6 Flash
No comparable hosted API rate
Granite-4.0-H-1B
Not sourced
Ling 2.6 Flash
Not sourced
Granite-4.0-H-1B
Not sourced
Ling 2.6 Flash
Not sourced
Granite-4.0-H-1B
Not sourced
Ling 2.6 Flash
Not sourced
Granite-4.0-H-1B
Non-Reasoning
Ling 2.6 Flash
Non-Reasoning
Granite-4.0-H-1B
Open Weight
Ling 2.6 Flash
Open Weight
Granite-4.0-H-1B
Open Weight
Ling 2.6 Flash
Open Weight
Granite-4.0-H-1B
2025-10-28
Ling 2.6 Flash
2026-04-21
Run the same representative tasks against both endpoints before changing production traffic.
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Granite-4.0-H-1B scores higher for coding on the public lane, 44.1 to 42.7. Granite-4.0-H-1B and Ling 2.6 Flash are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Granite-4.0-H-1B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Ling 2.6 Flash has the larger documented context window: 262K, compared with 128K.
Last updated September 4, 2026
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