Coding work
Code generation, repair, and software-engineering tasks
GPT-5.6 Luna
GPT-5.6 Luna leads on the public coding lane, 64.5 to 47.4, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
GPT-5.6 Luna vs Kimi K2.6
Updated September 24, 2026. Rank says GPT-5.6 Luna is ahead. Price, access, and your workload can each overturn that. 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
GPT-5.6 Luna has the higher public score estimate, 65.6 versus 59.12, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 14 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 14
- GPT-5.6 Luna only
- 15
- Kimi K2.6 only
- 23
- Like-for-like categories
- 3 / 8
Supported: GPT-5.6 Luna · Estimated: Kimi K2.6How the comparison works
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.
Agentic work
Tool use, computer use, and multi-step task completion
GPT-5.6 Luna
GPT-5.6 Luna leads on the public agentic lane, 55.3 to 43.6, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limitedLong 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
Chat turn cost
1K fresh input + 500 output tokens
GPT-5.6 Luna
GPT-5.6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-5.6 Luna
GPT-5.6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesRepository review cost
50K fresh input + 3K output tokens
GPT-5.6 Luna
GPT-5.6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
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.
64.5GPT-5.6 Luna47.4Kimi K2.6
Like-for-like · BenchAlign v5.7
GPT-5.6 Luna leads the like-for-like coding row.
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.
15 GPT-5.6 Luna only
14 Shared
23 Kimi K2.6 only
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
FrontierMath v2 (Tier 4)Math
Normalized gap 43.9GPT-5.6 Luna:58.500%Kimi K2.6:14.580%OSWorld 2.0Agentic
Normalized gap 41.0GPT-5.6 Luna:45.6%Kimi K2.6:4.6%FrontierMath v2 (Tiers 1-3)Math
Normalized gap 39.6GPT-5.6 Luna:78.600%Kimi K2.6:38.966%SWE-bench ProCoding
Normalized gap 4.1GPT-5.6 Luna:62.7%Kimi K2.6:58.6%GPQAKnowledge
Normalized gap 1.8GPT-5.6 Luna:92.3%Kimi K2.6:90.5%
Bars run 0–100 on each benchmark’s normalized display scale
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
Like-for-like- GPT-5.6 Luna
- 55.3
- Supported · #28/105
- Kimi K2.6
- 43.6
- Supported · #41/105
- Basis
- BenchAlign v5.7 lane · 9 vs 12 public rows
- Reading
- GPT-5.6 Luna leads · intervals overlap
Coding
Like-for-like- GPT-5.6 Luna
- 64.5
- Supported · #9/135
- Kimi K2.6
- 47.4
- Supported · #42/135
- Basis
- BenchAlign v5.7 lane · 7 vs 10 public rows
- Reading
- GPT-5.6 Luna leads
Knowledge
Like-for-like- GPT-5.6 Luna
- 64.6
- Supported · #22/158
- Kimi K2.6
- 59.0
- Supported · #35/158
- Basis
- BenchAlign v5.7 lane · 6 vs 5 public rows
- Reading
- GPT-5.6 Luna leads · intervals overlap
Multimodal
Directional only- GPT-5.6 Luna
- 67.1
- #22/50
- Kimi K2.6
- 63.9
- #27/50
- Basis
- Provisional lane · 1 vs 2 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.6 Luna
- 54.7
- #18/19
- Kimi K2.6
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.6 Luna
- Not ranked
- Kimi K2.6
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.6 Luna
- Not ranked
- Kimi K2.6
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.6 Luna
- 94.2
- Unranked · 3 rankable rows
- Kimi K2.6
- 71.0
- #1/7
- Basis
- Provisional lane · 2 vs 4 weighted rows
- Reading
- Not comparable
| Category | GPT-5.6 Luna | Kimi K2.6 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 55.3Supported · #28/105 | 43.6Supported · #41/105 | Like-for-likeBenchAlign v5.7 lane · 9 vs 12 public rows | GPT-5.6 Luna leads · intervals overlap |
| Coding | 64.5Supported · #9/135 | 47.4Supported · #42/135 | Like-for-likeBenchAlign v5.7 lane · 7 vs 10 public rows | GPT-5.6 Luna leads |
| Knowledge | 64.6Supported · #22/158 | 59.0Supported · #35/158 | Like-for-likeBenchAlign v5.7 lane · 6 vs 5 public rows | GPT-5.6 Luna leads · intervals overlap |
| Multimodal | 67.1#22/50 | 63.9#27/50 | Directional onlyProvisional lane · 1 vs 2 weighted rows | Directional only |
| Reasoning | 54.7#18/19 | 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 |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 94.2Unranked · 3 rankable rows | 71.0#1/7 | Not comparableProvisional lane · 2 vs 4 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.
Supported evidence per lane · 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
- GPT-5.6 Luna
- $0.0008
- Fits in one request
- Kimi K2.6
- $0.00295
- Fits in one request
GPT-5.6 Luna has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-5.6 Luna
- $0.0136
- Fits in one request
- Kimi K2.6
- $0.0595
- Fits in one request
GPT-5.6 Luna has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-5.6 Luna
- $0.02
- Fits in one request
- Kimi K2.6
- $0.091
- Fits in one request
GPT-5.6 Luna has the lower modeled cost
Costs use the listed standard API rates.
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.
SpecificationGPT-5.6 LunaKimi K2.6
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.6 Luna
1.05M
OpenAI model catalogKimi K2.6
256K
API model ID
GPT-5.6 Luna
gpt-5.6-luna
OpenAI model catalogKimi K2.6
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 pricingKimi K2.6
$0.16 per 1M cached input tokens
Documented inputs
GPT-5.6 Luna
text, image
OpenAI model catalogKimi K2.6
Not sourced
Documented outputs
GPT-5.6 Luna
Kimi K2.6
Not sourced
Provider availability
GPT-5.6 Luna
Generally Available · OpenAI Responses API
OpenAI model catalogKimi K2.6
Not sourced
Reasoning profile
GPT-5.6 Luna
Reasoning
Kimi K2.6
Reasoning
Weight access
GPT-5.6 Luna
Proprietary
Kimi K2.6
Open Weight
License
GPT-5.6 Luna
Proprietary
Kimi K2.6
Open Weight
Release date
GPT-5.6 Luna
2026-07-09
Kimi K2.6
2026-04-20
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
- GPT-5.6 Luna has the higher public score estimate, 65.6 versus 59.12, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.0136 vs $0.0595. Cache-heavy agent loop: $0.02 vs $0.091.
- 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.
Questions
Which is better, GPT-5.6 Luna or Kimi K2.6?
GPT-5.6 Luna has the higher public score estimate, 65.6 versus 59.12, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, GPT-5.6 Luna or Kimi K2.6?
GPT-5.6 Luna leads the public coding lane, 64.5 to 47.4, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, GPT-5.6 Luna or Kimi K2.6?
GPT-5.6 Luna leads the public agentic tasks lane, 55.3 to 43.6, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, GPT-5.6 Luna or Kimi K2.6?
For the stated presets, chat costs $0.0008 on GPT-5.6 Luna and $0.00295 on Kimi K2.6; repository review costs $0.0136 and $0.0595; the cache-heavy agent loop costs $0.02 and $0.091. Costs use the listed standard API rates.
Which has the larger context window, GPT-5.6 Luna or Kimi K2.6?
GPT-5.6 Luna has the larger documented context window: 1.05M, compared with 256K.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
GPT-5.6 Luna
API / mo$1,050
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Kimi K2.6
API / mo$3,713
Self-host / mo$18,221
Break-even326M/day
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence52 rows
Agentic
Terminal-Bench 3.0
GPT-5.6 Luna14.3%SourceKimi K2.6—Not directly comparable
Terminal-Bench 2.1
GPT-5.6 Luna84.7%SourceKimi K2.6—Not directly comparable
BrowseComp
GPT-5.6 Luna83.3%SourceKimi K2.683.2%SourceGPT-5.6 Luna leads this result
OSWorld 2.0
GPT-5.6 Luna45.6%SourceKimi K2.64.6%SourceGPT-5.6 Luna leads this result
CyberGym
GPT-5.6 Luna77.9%SourceKimi K2.6—Not directly comparable
ExploitGym
GPT-5.6 Luna12.4%SourceKimi K2.6—Not directly comparable
Toolathlon
GPT-5.6 Luna53.4%SourceKimi K2.650%SourceGPT-5.6 Luna leads this result
Terminal-Bench 2.1 (Vals)
GPT-5.6 Luna79.0%SourceKimi K2.653.6%SourceGPT-5.6 Luna leads this result
ApprenticeBench
GPT-5.6 Luna7%SourceKimi K2.6—Not directly comparable
Terminal-Bench 2.0
GPT-5.6 Luna—Kimi K2.666.7%SourceNot directly comparable
OSWorld-Verified
GPT-5.6 Luna—Kimi K2.673.1%SourceNot directly comparable
MCP Atlas
GPT-5.6 Luna—Kimi K2.655.9%SourceNot directly comparable
Claw-Eval
GPT-5.6 Luna—Kimi K2.662.3%SourceNot directly comparable
DeepSearchQA
GPT-5.6 Luna—Kimi K2.692.5%SourceNot directly comparable
WideResearch
GPT-5.6 Luna—Kimi K2.680.8%SourceNot directly comparable
Gert Labs
GPT-5.6 Luna—Kimi K2.656.82%SourceNot directly comparable
ResearchClawBench
GPT-5.6 Luna—Kimi K2.618.0%SourceNot directly comparable
Coding
SWE-bench Pro
GPT-5.6 Luna62.7%SourceKimi K2.658.6%SourceGPT-5.6 Luna leads this result
Terminal-Bench 2.1
GPT-5.6 Luna84.7%SourceKimi K2.6—Not directly comparable
DeepSWE
GPT-5.6 Luna67.2%SourceKimi K2.6—Not directly comparable
FrontierCode 1.1 Extended
GPT-5.6 Luna55.1%SourceKimi K2.6—Not directly comparable
cursorBench32
GPT-5.6 Luna61.1%SourceKimi K2.6—Not directly comparable
VulcanBench v3
GPT-5.6 Luna85.5%SourceKimi K2.6—Not directly comparable
SWE-bench (Vals)
GPT-5.6 Luna93.0%SourceKimi K2.676.2%SourceGPT-5.6 Luna leads this result
SWE-bench Verified
GPT-5.6 Luna—Kimi K2.680.2%SourceNot directly comparable
LiveCodeBench v6
GPT-5.6 Luna—Kimi K2.689.6%SourceNot directly comparable
SWE Multilingual
GPT-5.6 Luna—Kimi K2.676.7%SourceNot directly comparable
SciCode
GPT-5.6 Luna—Kimi K2.652.2%SourceNot directly comparable
Terminal-Bench 2.0
GPT-5.6 Luna—Kimi K2.666.7%SourceNot directly comparable
Vibe Code Bench
GPT-5.6 Luna—Kimi K2.637.89%SourceNot directly comparable
cursorBench31
GPT-5.6 Luna—Kimi K2.647.6%SourceNot directly comparable
LiveCodeBench (Vals)
GPT-5.6 Luna—Kimi K2.686.8%SourceNot directly comparable
Reasoning
Multimodal
MMMU-Pro
GPT-5.6 Luna78.4%SourceKimi K2.679.4%SourceKimi K2.6 leads this result
MMMU-Pro w/ Python
GPT-5.6 Luna79.5%SourceKimi K2.680.1%SourceKimi K2.6 leads this result
CharXiv
GPT-5.6 Luna—Kimi K2.680.4%SourceNot directly comparable
MathVision
GPT-5.6 Luna—Kimi K2.687.4%SourceNot directly comparable
V*
GPT-5.6 Luna—Kimi K2.696.9%SourceNot directly comparable
Knowledge
GPQA
GPT-5.6 Luna92.3%SourceKimi K2.690.5%SourceGPT-5.6 Luna leads this result
GPQA-D
GPT-5.6 Luna92.3%SourceKimi K2.690.5%SourceGPT-5.6 Luna leads this result
HealthBench Professional
GPT-5.6 Luna55.7%SourceKimi K2.6—Not directly comparable
HealthBench Hard
GPT-5.6 Luna32.0%SourceKimi K2.6—Not directly comparable
GPQA Diamond (Vals)
GPT-5.6 Luna91.7%SourceKimi K2.689.1%SourceGPT-5.6 Luna leads this result
MMLU-Pro (Vals)
GPT-5.6 Luna86.0%SourceKimi K2.687.6%SourceKimi K2.6 leads this result
HLE
GPT-5.6 Luna—Kimi K2.634.7%SourceNot directly comparable
Math
FrontierMath (legacy)
GPT-5.6 Luna78.6%SourceKimi K2.6—Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.6 Luna78.600%SourceKimi K2.638.966%SourceGPT-5.6 Luna leads this result
FrontierMath v2 (Tier 4)
GPT-5.6 Luna58.500%SourceKimi K2.614.580%SourceGPT-5.6 Luna leads this result
AIME26
GPT-5.6 Luna—Kimi K2.696.4%SourceNot directly comparable
HMMT Feb 2026
GPT-5.6 Luna—Kimi K2.692.7%SourceNot directly comparable
MMAnswerBench
GPT-5.6 Luna—Kimi K2.686.0%SourceNot directly comparable
52 public results · 14 shared
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Last updated September 24, 2026