Agentic
Directional only- Ling 2.6 Flash
- 39.9
- Estimated · #122/151
- Llama 4 Scout
- 33.1
- Estimated · #134/151
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Directional only
Ling 2.6 Flash vs Llama 4 Scout
Updated 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
Which one for your work
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.
Long documents
Prompts that approach the documented context limit
Llama 4 Scout
Llama 4 Scout has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Ling 2.6 Flash and Llama 4 Scout are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Ling 2.6 Flash and Llama 4 Scout are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Chat turn cost
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
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Repository review cost
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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 Llama 4 Scout only
- Shared results
- 0
- Ling 2.6 Flash only
- 0
- Llama 4 Scout only
- 1
- Like-for-like categories
- 0 / 8
4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
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.
Coding
Directional only- Ling 2.6 Flash
- 42.7
- Estimated · #127/183
- Llama 4 Scout
- 32.9
- Estimated · #160/183
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Directional only
Knowledge
Directional only- Ling 2.6 Flash
- 41.4
- Estimated · #132/181
- Llama 4 Scout
- 34.2
- Estimated · #169/181
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Directional only
Instruction following
Directional only- Ling 2.6 Flash
- 46.6
- #86/120
- Llama 4 Scout
- 45.7
- #89/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- Ling 2.6 Flash
- 41.2
- Unranked · 2 rankable rows
- Llama 4 Scout
- 42.8
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Ling 2.6 Flash
- Not ranked
- Llama 4 Scout
- 25.3
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Ling 2.6 Flash
- Not ranked
- Llama 4 Scout
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Ling 2.6 Flash
- Not ranked
- Llama 4 Scout
- 37.7
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Ling 2.6 Flash | Llama 4 Scout | Basis | Reading |
|---|---|---|---|---|
| Agentic | 39.9Estimated · #122/151 | 33.1Estimated · #134/151 | Directional onlyBenchAlign lane · 0 vs 0 public rows | Directional only |
| Coding | 42.7Estimated · #127/183 | 32.9Estimated · #160/183 | Directional onlyBenchAlign lane · 0 vs 0 public rows | Directional only |
| Knowledge | 41.4Estimated · #132/181 | 34.2Estimated · #169/181 | Directional onlyBenchAlign lane · 0 vs 0 public rows | Directional only |
| Instruction following | 46.6#86/120 | 45.7#89/120 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 41.2Unranked · 2 rankable rows | 42.8Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 25.3Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 37.7Unranked · 1 rankable row | 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.
Shape of the matched evidence
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.
What each workload costs
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.
Chat turn
1K fresh input + 500 output tokens
- Ling 2.6 Flash
- API rate not published
- Fits in one request
- Llama 4 Scout
- Self-hosted; infrastructure cost varies
- Fits in one request
Ling 2.6 Flash has no comparable published API token rate. Llama 4 Scout has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Ling 2.6 Flash
- API rate not published
- Fits in one request
- Llama 4 Scout
- Self-hosted; infrastructure cost varies
- Fits in one request
Ling 2.6 Flash has no comparable published API token rate. Llama 4 Scout has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Ling 2.6 Flash
- API rate not published
- Fits in one request
- Cached-input rate unavailable
- Llama 4 Scout
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
Ling 2.6 Flash has no comparable published API token rate. Llama 4 Scout has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationLing 2.6 FlashLlama 4 Scout
Context window
Maximum documented context; output-token limits may be lower.
Ling 2.6 Flash
262K
Llama 4 Scout
10M
API model ID
Ling 2.6 Flash
Not sourced
Llama 4 Scout
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Ling 2.6 Flash
No comparable hosted API rate
Llama 4 Scout
No comparable hosted API rate
Documented inputs
Ling 2.6 Flash
Not sourced
Llama 4 Scout
Not sourced
Documented outputs
Ling 2.6 Flash
Not sourced
Llama 4 Scout
Not sourced
Provider availability
Ling 2.6 Flash
Not sourced
Llama 4 Scout
Not sourced
Reasoning profile
Ling 2.6 Flash
Non-Reasoning
Llama 4 Scout
Non-Reasoning
Weight access
Ling 2.6 Flash
Open Weight
Llama 4 Scout
Open Weight
License
Ling 2.6 Flash
Open Weight
Llama 4 Scout
Open Weight
Release date
Ling 2.6 Flash
2026-04-21
Llama 4 Scout
2026-02-28
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Llama 4 Scout has the larger documented window (10M).
Run the same representative tasks against both endpoints before changing production traffic.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Ling 2.6 Flash
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Llama 4 Scout
API / mo$0
Self-host / mo$2,278
Break-even—
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence1 rows
Math
FrontierMath v2 (Tiers 1-3)
Ling 2.6 Flash—Llama 4 Scout0.000%SourceNot directly comparable
Frequently asked questions
Which is better, Ling 2.6 Flash or Llama 4 Scout?
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.
Which is better for coding, Ling 2.6 Flash or Llama 4 Scout?
Ling 2.6 Flash scores higher for coding on the public lane, 42.7 to 32.9. Ling 2.6 Flash and Llama 4 Scout 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.
Which is better for agentic tasks, Ling 2.6 Flash or Llama 4 Scout?
Ling 2.6 Flash scores higher for agentic tasks on the public lane, 39.9 to 33.1. Ling 2.6 Flash and Llama 4 Scout are scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, Ling 2.6 Flash or Llama 4 Scout?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, Ling 2.6 Flash or Llama 4 Scout?
Llama 4 Scout has the larger documented context window: 10M, compared with 262K.
Related comparisons
Last updated September 4, 2026
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