Agentic
Not comparable- GPT-5.6 Luna
- 56.7
- Supported · #34/154
- Ling 3.0 Flash FP8
- Not ranked
- Basis
- BenchAlign lane · 9 vs 0 public rows
- Reading
- Not comparable
GPT-5.6 Luna vs Ling 3.0 Flash FP8
Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Updated September 18, 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
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
GPT-5.6 Luna
GPT-5.6 Luna 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 3.0 Flash FP8 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Ling 3.0 Flash FP8 is not ranked on the public lane for agentic, so no winner is named for agentic.
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
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.
66.8GPT-5.6 Luna—Ling 3.0 Flash FP8
Not comparable · BenchAlign
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.
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
28 GPT-5.6 Luna only
2 Shared
2 Ling 3.0 Flash FP8 only
- Shared results
- 2
- GPT-5.6 Luna only
- 28
- Ling 3.0 Flash FP8 only
- 2
- Like-for-like categories
- 0 / 8
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
Not comparable- GPT-5.6 Luna
- 66.8
- Supported · #9/154
- Ling 3.0 Flash FP8
- Not ranked
- Basis
- BenchAlign lane · 8 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.6 Luna
- 51.2
- #19/20
- Ling 3.0 Flash FP8
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5.6 Luna
- 64.2
- Supported · #27/184
- Ling 3.0 Flash FP8
- Not ranked
- Basis
- BenchAlign lane · 6 vs 2 public rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.6 Luna
- 94.2
- Unranked · 3 rankable rows
- Ling 3.0 Flash FP8
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.6 Luna
- Not ranked
- Ling 3.0 Flash FP8
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.6 Luna
- 67.1
- #21/48
- Ling 3.0 Flash FP8
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.6 Luna
- Not ranked
- Ling 3.0 Flash FP8
- 69.7
- #65/124
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
| Category | GPT-5.6 Luna | Ling 3.0 Flash FP8 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 56.7Supported · #34/154 | Not ranked | Not comparableBenchAlign lane · 9 vs 0 public rows | Not comparable |
| Coding | 66.8Supported · #9/154 | Not ranked | Not comparableBenchAlign lane · 8 vs 1 public rows | Not comparable |
| Reasoning | 51.2#19/20 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Knowledge | 64.2Supported · #27/184 | Not ranked | Not comparableBenchAlign lane · 6 vs 2 public rows | Not comparable |
| Math | 94.2Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 67.1#21/48 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 69.7#65/124 | Not comparableProvisional lane · 0 vs 1 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.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
GPQA
Knowledge
GPT-5.6 Luna: 92.3%Ling 3.0 Flash FP8: 84%Normalized gap 8.3GPT-5.6 Luna source Ling 3.0 Flash FP8 source
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
- GPT-5.6 Luna
- $0.0008
- Fits in one request
- Ling 3.0 Flash FP8
- API rate not published
- Fits in one request
Ling 3.0 Flash FP8 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GPT-5.6 Luna
- $0.0136
- Fits in one request
- Ling 3.0 Flash FP8
- API rate not published
- Fits in one request
Ling 3.0 Flash FP8 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-5.6 Luna
- $0.02
- Fits in one request
- Ling 3.0 Flash FP8
- API rate not published
- Fits in one request
- Cached-input rate unavailable
Ling 3.0 Flash FP8 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.
SpecificationGPT-5.6 LunaLing 3.0 Flash FP8
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.6 Luna
1.05M
OpenAI model catalogLing 3.0 Flash FP8
API model ID
GPT-5.6 Luna
gpt-5.6-luna
OpenAI model catalogLing 3.0 Flash FP8
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.6 Luna
$0.02 per 1M cached input tokens
OpenAI pricingLing 3.0 Flash FP8
No comparable hosted API rate
InclusionAI Ling 3.0 Flash FP8 model cardDocumented inputs
GPT-5.6 Luna
text, image
OpenAI model catalogLing 3.0 Flash FP8
Not sourced
Documented outputs
GPT-5.6 Luna
Ling 3.0 Flash FP8
Not sourced
Provider availability
GPT-5.6 Luna
Generally Available · OpenAI Responses API
OpenAI model catalogLing 3.0 Flash FP8
Not sourced
Reasoning profile
GPT-5.6 Luna
Reasoning
Ling 3.0 Flash FP8
Reasoning
Weight access
GPT-5.6 Luna
Proprietary
Ling 3.0 Flash FP8
Open Weight
License
GPT-5.6 Luna
Proprietary
Ling 3.0 Flash FP8
Open Weight
Release date
GPT-5.6 Luna
2026-07-09
Ling 3.0 Flash FP8
2026-08-04
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- GPT-5.6 Luna has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence32 rows
Agentic
Terminal-Bench 3.0
GPT-5.6 Luna14.3%SourceLing 3.0 Flash FP8—Not directly comparable
Terminal-Bench 2.0
GPT-5.6 Luna84.7%SourceLing 3.0 Flash FP8—Not directly comparable
BrowseComp
GPT-5.6 Luna83.3%SourceLing 3.0 Flash FP8—Not directly comparable
OSWorld 2.0
GPT-5.6 Luna45.6%SourceLing 3.0 Flash FP8—Not directly comparable
CyberGym
GPT-5.6 Luna77.9%SourceLing 3.0 Flash FP8—Not directly comparable
ExploitGym
GPT-5.6 Luna12.4%SourceLing 3.0 Flash FP8—Not directly comparable
Toolathlon
GPT-5.6 Luna53.4%SourceLing 3.0 Flash FP8—Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.6 Luna79.0%SourceLing 3.0 Flash FP8—Not directly comparable
ApprenticeBench
GPT-5.6 Luna7%SourceLing 3.0 Flash FP8—Not directly comparable
Coding
SWE-bench Pro
GPT-5.6 Luna62.7%SourceLing 3.0 Flash FP8—Not directly comparable
Terminal-Bench 2.0
GPT-5.6 Luna84.7%SourceLing 3.0 Flash FP8—Not directly comparable
DeepSWE
GPT-5.6 Luna67.2%SourceLing 3.0 Flash FP8—Not directly comparable
FrontierCode 1.1 Extended
GPT-5.6 Luna55.1%SourceLing 3.0 Flash FP8—Not directly comparable
cursorBench32
GPT-5.6 Luna61.1%SourceLing 3.0 Flash FP8—Not directly comparable
VulcanBench v3
GPT-5.6 Luna85.5%SourceLing 3.0 Flash FP8—Not directly comparable
SWE-bench (Vals)
GPT-5.6 Luna93.0%SourceLing 3.0 Flash FP8—Not directly comparable
cursorBench40
GPT-5.6 Luna35.9%SourceLing 3.0 Flash FP8—Not directly comparable
SciCode
GPT-5.6 Luna—Ling 3.0 Flash FP840.4%SourceNot directly comparable
Reasoning
Knowledge
GPQA
GPT-5.6 Luna92.3%SourceLing 3.0 Flash FP884%SourceGPT-5.6 Luna leads this result
GPQA-D
GPT-5.6 Luna92.3%SourceLing 3.0 Flash FP884.0%SourceGPT-5.6 Luna leads this result
HealthBench Professional
GPT-5.6 Luna55.7%SourceLing 3.0 Flash FP8—Not directly comparable
HealthBench Hard
GPT-5.6 Luna32.0%SourceLing 3.0 Flash FP8—Not directly comparable
GPQA Diamond (Vals)
GPT-5.6 Luna91.7%SourceLing 3.0 Flash FP8—Not directly comparable
MMLU-Pro (Vals)
GPT-5.6 Luna86.0%SourceLing 3.0 Flash FP8—Not directly comparable
Math
Multimodal
Instruction following
IFBench
GPT-5.6 Luna—Ling 3.0 Flash FP873.4%SourceNot directly comparable
Questions
Which is better, GPT-5.6 Luna or Ling 3.0 Flash FP8?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-5.6 Luna or Ling 3.0 Flash FP8?
Ling 3.0 Flash FP8 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GPT-5.6 Luna or Ling 3.0 Flash FP8?
Ling 3.0 Flash FP8 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-5.6 Luna or Ling 3.0 Flash FP8?
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, GPT-5.6 Luna or Ling 3.0 Flash FP8?
GPT-5.6 Luna has the larger documented context window: 1.05M, compared with 262K.
Related comparisons
Last updated September 18, 2026
Watch GPT-5.6 Luna vs Ling 3.0 Flash FP8
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.