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
Claude Mythos 5 vs GPT-5.6 Luna
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
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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. 6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 6
- Claude Mythos 5 only
- 9
- GPT-5.6 Luna only
- 23
- Like-for-like categories
- 0 / 8
Supported: GPT-5.6 LunaHow 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.
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-rates
Show secondary and unsupported calls
Repository 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-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Claude Mythos 5 is 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
Claude Mythos 5 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
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.
—Claude Mythos 564.5GPT-5.6 Luna
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.
9 Claude Mythos 5 only
6 Shared
23 GPT-5.6 Luna 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.
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.
SWE-bench ProCoding
Normalized gap 17.6Claude Mythos 5:80.3%GPT-5.6 Luna:62.7%BrowseCompAgentic
Normalized gap 4.7Claude Mythos 5:88%GPT-5.6 Luna:83.3%GPQAKnowledge
Normalized gap 1.8Claude Mythos 5:94.1%GPT-5.6 Luna:92.3%
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
Not comparable- Claude Mythos 5
- Not ranked
- GPT-5.6 Luna
- 55.3
- Supported · #28/105
- Basis
- BenchAlign v5.7 lane · 4 vs 9 public rows
- Reading
- Not comparable
Coding
Not comparable- Claude Mythos 5
- Not ranked
- GPT-5.6 Luna
- 64.5
- Supported · #9/135
- Basis
- BenchAlign v5.7 lane · 3 vs 7 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Claude Mythos 5
- Not ranked
- GPT-5.6 Luna
- 54.7
- #18/19
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Mythos 5
- 85.1
- Unranked · 3 rankable rows
- GPT-5.6 Luna
- 67.1
- #22/50
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Claude Mythos 5
- Not ranked
- GPT-5.6 Luna
- 64.6
- Supported · #22/158
- Basis
- BenchAlign v5.7 lane · 3 vs 6 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Mythos 5
- Not ranked
- GPT-5.6 Luna
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Mythos 5
- Not ranked
- GPT-5.6 Luna
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Mythos 5
- Not ranked
- GPT-5.6 Luna
- 94.2
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 1 vs 2 weighted rows
- Reading
- Not comparable
| Category | Claude Mythos 5 | GPT-5.6 Luna | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | 55.3Supported · #28/105 | Not comparableBenchAlign v5.7 lane · 4 vs 9 public rows | Not comparable |
| Coding | Not ranked | 64.5Supported · #9/135 | Not comparableBenchAlign v5.7 lane · 3 vs 7 public rows | Not comparable |
| Reasoning | Not ranked | 54.7#18/19 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multimodal | 85.1Unranked · 3 rankable rows | 67.1#22/50 | Not comparableProvisional lane · 1 vs 1 weighted rows | Not comparable |
| Knowledge | Not ranked | 64.6Supported · #22/158 | Not comparableBenchAlign v5.7 lane · 3 vs 6 public 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 | Not ranked | 94.2Unranked · 3 rankable rows | Not comparableProvisional lane · 1 vs 2 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
- Claude Mythos 5
- $0.035
- Fits in one request
- GPT-5.6 Luna
- $0.0008
- 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
- Claude Mythos 5
- $0.65
- Fits in one request
- GPT-5.6 Luna
- $0.0136
- 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
- Claude Mythos 5
- $0.9
- Fits in one request
- GPT-5.6 Luna
- $0.02
- 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.
SpecificationClaude Mythos 5GPT-5.6 Luna
Context window
Maximum documented context; output-token limits may be lower.
Claude Mythos 5
GPT-5.6 Luna
1.05M
OpenAI model catalogAPI model ID
Claude Mythos 5
claude-mythos-5
Anthropic model overviewGPT-5.6 Luna
gpt-5.6-luna
OpenAI model catalogCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Mythos 5
$1 per 1M cached input tokens
Claude API pricingGPT-5.6 Luna
$0.02 per 1M cached input tokens
OpenAI pricingDocumented inputs
Claude Mythos 5
text, image
Anthropic model overviewGPT-5.6 Luna
text, image
OpenAI model catalogDocumented outputs
Claude Mythos 5
GPT-5.6 Luna
Provider availability
Claude Mythos 5
Limited Access · Claude API
Anthropic model overviewGPT-5.6 Luna
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
Claude Mythos 5
Reasoning
GPT-5.6 Luna
Reasoning
Weight access
Claude Mythos 5
Proprietary
GPT-5.6 Luna
Proprietary
License
Claude Mythos 5
Proprietary
GPT-5.6 Luna
Proprietary
Release date
Claude Mythos 5
2026-06-09
GPT-5.6 Luna
2026-07-09
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
- Repository review: $0.65 vs $0.0136. Cache-heavy agent loop: $0.9 vs $0.02.
- 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, Claude Mythos 5 or GPT-5.6 Luna?
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, Claude Mythos 5 or GPT-5.6 Luna?
Claude Mythos 5 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Claude Mythos 5 or GPT-5.6 Luna?
Claude Mythos 5 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Claude Mythos 5 or GPT-5.6 Luna?
For the stated presets, chat costs $0.035 on Claude Mythos 5 and $0.0008 on GPT-5.6 Luna; repository review costs $0.65 and $0.0136; the cache-heavy agent loop costs $0.9 and $0.02. Costs use the listed standard API rates.
Which has the larger context window, Claude Mythos 5 or GPT-5.6 Luna?
GPT-5.6 Luna has the larger documented context window: 1.05M, compared with 1M.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence38 rows
Agentic
Terminal-Bench 2.1
Claude Mythos 588.0%SourceGPT-5.6 Luna84.7%SourceClaude Mythos 5 leads this result
OSWorld-Verified
Claude Mythos 585%SourceGPT-5.6 Luna—Not directly comparable
BrowseComp
Claude Mythos 588%SourceGPT-5.6 Luna83.3%SourceClaude Mythos 5 leads this result
CyberGym
Claude Mythos 583.8%SourceGPT-5.6 Luna77.9%SourceClaude Mythos 5 leads this result
Terminal-Bench 3.0
Claude Mythos 5—GPT-5.6 Luna14.3%SourceNot directly comparable
OSWorld 2.0
Claude Mythos 5—GPT-5.6 Luna45.6%SourceNot directly comparable
ExploitGym
Claude Mythos 5—GPT-5.6 Luna12.4%SourceNot directly comparable
Toolathlon
Claude Mythos 5—GPT-5.6 Luna53.4%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
Claude Mythos 5—GPT-5.6 Luna79.0%SourceNot directly comparable
ApprenticeBench
Claude Mythos 5—GPT-5.6 Luna7%SourceNot directly comparable
Coding
SWE-bench Verified
Claude Mythos 595.5%SourceGPT-5.6 Luna—Not directly comparable
SWE-bench Pro
Claude Mythos 580.3%SourceGPT-5.6 Luna62.7%SourceClaude Mythos 5 leads this result
Terminal-Bench 2.1
Claude Mythos 588.0%SourceGPT-5.6 Luna84.7%SourceClaude Mythos 5 leads this result
DeepSWE
Claude Mythos 5—GPT-5.6 Luna67.2%SourceNot directly comparable
FrontierCode 1.1 Extended
Claude Mythos 5—GPT-5.6 Luna55.1%SourceNot directly comparable
cursorBench32
Claude Mythos 5—GPT-5.6 Luna61.1%SourceNot directly comparable
VulcanBench v3
Claude Mythos 5—GPT-5.6 Luna85.5%SourceNot directly comparable
SWE-bench (Vals)
Claude Mythos 5—GPT-5.6 Luna93.0%SourceNot directly comparable
Reasoning
Multimodal
SWE-bench Multimodal
Claude Mythos 554.9%SourceGPT-5.6 Luna—Not directly comparable
CharXiv
Claude Mythos 593.5%SourceGPT-5.6 Luna—Not directly comparable
CharXiv w/o tools
Claude Mythos 588.9%SourceGPT-5.6 Luna—Not directly comparable
MMMU-Pro
Claude Mythos 5—GPT-5.6 Luna78.4%SourceNot directly comparable
MMMU-Pro w/ Python
Claude Mythos 5—GPT-5.6 Luna79.5%SourceNot directly comparable
Knowledge
GPQA
Claude Mythos 594.1%SourceGPT-5.6 Luna92.3%SourceClaude Mythos 5 leads this result
HLE
Claude Mythos 564.5%SourceGPT-5.6 Luna—Not directly comparable
HLE w/o tools
Claude Mythos 559%SourceGPT-5.6 Luna—Not directly comparable
GPQA-D
Claude Mythos 5—GPT-5.6 Luna92.3%SourceNot directly comparable
HealthBench Professional
Claude Mythos 5—GPT-5.6 Luna55.7%SourceNot directly comparable
HealthBench Hard
Claude Mythos 5—GPT-5.6 Luna32.0%SourceNot directly comparable
GPQA Diamond (Vals)
Claude Mythos 5—GPT-5.6 Luna91.7%SourceNot directly comparable
MMLU-Pro (Vals)
Claude Mythos 5—GPT-5.6 Luna86.0%SourceNot directly comparable
Multilingual
SWE Multilingual
Claude Mythos 592.2%SourceGPT-5.6 Luna—Not directly comparable
Math
USAMO 2026
Claude Mythos 597.6%SourceGPT-5.6 Luna—Not directly comparable
FrontierMath (legacy)
Claude Mythos 5—GPT-5.6 Luna78.6%SourceNot directly comparable
FrontierMath v2 (Tiers 1-3)
Claude Mythos 5—GPT-5.6 Luna78.600%SourceNot directly comparable
FrontierMath v2 (Tier 4)
Claude Mythos 5—GPT-5.6 Luna58.500%SourceNot directly comparable
38 public results · 6 shared
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Last updated September 24, 2026