Long documents
Prompts that approach the documented context limit
Ling 3.0 Flash FP8
Ling 3.0 Flash FP8 has the larger documented context window.
Confidence: documented
Ling 3.0 Flash FP8 vs Macaw
Updated September 24, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
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.
- Shared results
- 0
- Ling 3.0 Flash FP8 only
- 4
- Macaw only
- 0
- Like-for-like categories
- 0 / 8
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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Ling 3.0 Flash FP8 and Macaw are not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Ling 3.0 Flash FP8 and Macaw are not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedChat 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-ratesCache-heavy agent loop cost
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. Macaw does not fit this workload in one request. Ling 3.0 Flash FP8 has no comparable published API token rate. Macaw has no comparable published API token rate.
Confidence: listed-ratesRepository 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
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
—Ling 3.0 Flash FP8—Macaw
Not comparable · BenchAlign v5.7
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
4 Ling 3.0 Flash FP8 only
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.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.7 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.
Agentic
Not comparable- Ling 3.0 Flash FP8
- Not ranked
- Macaw
- Not ranked
- Basis
- BenchAlign v5.7 lane · 0 vs 0 public rows
- Reading
- Not comparable
Coding
Not comparable- Ling 3.0 Flash FP8
- Not ranked
- Macaw
- Not ranked
- Basis
- BenchAlign v5.7 lane · 1 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Ling 3.0 Flash FP8
- Not ranked
- Macaw
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Ling 3.0 Flash FP8
- Not ranked
- Macaw
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Ling 3.0 Flash FP8
- Not ranked
- Macaw
- Not ranked
- Basis
- BenchAlign v5.7 lane · 2 vs 0 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Ling 3.0 Flash FP8
- Not ranked
- Macaw
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Ling 3.0 Flash FP8
- 69.7
- #65/124
- Macaw
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Ling 3.0 Flash FP8
- Not ranked
- Macaw
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Ling 3.0 Flash FP8 | Macaw | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign v5.7 lane · 0 vs 0 public rows | Not comparable |
| Coding | Not ranked | Not ranked | Not comparableBenchAlign v5.7 lane · 1 vs 0 public rows | Not comparable |
| Reasoning | 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 |
| Knowledge | Not ranked | Not ranked | Not comparableBenchAlign v5.7 lane · 2 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 69.7#65/124 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Math | 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 v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Bars run 0–100Methodology
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 3.0 Flash FP8
- API rate not published
- Fits in one request
- Macaw
- Self-hosted; infrastructure cost varies
- Fits in one request
Ling 3.0 Flash FP8 has no comparable published API token rate. Macaw has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Ling 3.0 Flash FP8
- API rate not published
- Fits in one request
- Macaw
- Self-hosted; infrastructure cost varies
- Fits in one request
Ling 3.0 Flash FP8 has no comparable published API token rate. Macaw has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Ling 3.0 Flash FP8
- API rate not published
- Fits in one request
- Cached-input rate unavailable
- Macaw
- Self-hosted; infrastructure cost varies
- Does not fit in one request
- Cached-input rate unavailable
Macaw does not fit this workload in one request. Ling 3.0 Flash FP8 has no comparable published API token rate. Macaw has no comparable published API token rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationLing 3.0 Flash FP8Macaw
Context window
Maximum documented context; output-token limits may be lower.
Ling 3.0 Flash FP8
Macaw
API model ID
Ling 3.0 Flash FP8
Not sourced
Macaw
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Ling 3.0 Flash FP8
No comparable hosted API rate
InclusionAI Ling 3.0 Flash FP8 model cardMacaw
No comparable hosted API rate
Bad Theory Labs Macaw model cardDocumented inputs
Ling 3.0 Flash FP8
Not sourced
Macaw
Not sourced
Documented outputs
Ling 3.0 Flash FP8
Not sourced
Macaw
Not sourced
Provider availability
Ling 3.0 Flash FP8
Not sourced
Macaw
Not sourced
Reasoning profile
Ling 3.0 Flash FP8
Reasoning
Macaw
Reasoning
Weight access
Ling 3.0 Flash FP8
Open Weight
Macaw
Open Weight
License
Ling 3.0 Flash FP8
Open Weight
Macaw
Open Weight
Release date
Ling 3.0 Flash FP8
2026-08-04
Macaw
2026-08-05
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
- Ling 3.0 Flash FP8 has the larger documented window (262K).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Ling 3.0 Flash FP8 or Macaw?
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 3.0 Flash FP8 or Macaw?
Ling 3.0 Flash FP8 and Macaw are not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Ling 3.0 Flash FP8 or Macaw?
Ling 3.0 Flash FP8 and Macaw are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Ling 3.0 Flash FP8 or Macaw?
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 3.0 Flash FP8 or Macaw?
Ling 3.0 Flash FP8 has the larger documented context window: 262K, compared with 128K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence4 rows
4 public results · 0 shared
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Last updated September 24, 2026