Agentic
Not comparable- Claude Mythos Preview
- Not ranked
- GPT-6 Luna
- 56.7
- Estimated · #35/157
- Basis
- BenchAlign lane · 2 vs 1 public rows
- Reading
- Not comparable
Claude Mythos Preview vs GPT-6 Luna
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.
1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Updated September 22, 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.
Chat turn cost
1K fresh input + 500 output tokens
GPT-6 Luna
GPT-6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-6 Luna
GPT-6 Luna has the lower estimated token cost for this stated workload. Claude Mythos Preview has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
GPT-6 Luna
GPT-6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Claude Mythos Preview 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
Claude Mythos Preview is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Not enough matched evidence
A complete context comparison is not sourced.
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 Preview66.6GPT-6 Luna
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.
1 Claude Mythos Preview only
1 Shared
6 GPT-6 Luna only
- Shared results
- 1
- Claude Mythos Preview only
- 1
- GPT-6 Luna only
- 6
- 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- Claude Mythos Preview
- Not ranked
- GPT-6 Luna
- 66.6
- Estimated · #11/159
- Basis
- BenchAlign lane · 0 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Claude Mythos Preview
- Not ranked
- GPT-6 Luna
- 78.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Mythos Preview
- Not ranked
- GPT-6 Luna
- 71.3
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Claude Mythos Preview
- Not ranked
- GPT-6 Luna
- 63.6
- Estimated · #29/189
- Basis
- BenchAlign lane · 0 vs 5 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Mythos Preview
- Not ranked
- GPT-6 Luna
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Mythos Preview
- Not ranked
- GPT-6 Luna
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Mythos Preview
- Not ranked
- GPT-6 Luna
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Mythos Preview | GPT-6 Luna | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | 56.7Estimated · #35/157 | Not comparableBenchAlign lane · 2 vs 1 public rows | Not comparable |
| Coding | Not ranked | 66.6Estimated · #11/159 | Not comparableBenchAlign lane · 0 vs 1 public rows | Not comparable |
| Reasoning | Not ranked | 78.3Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 71.3Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | Not ranked | 63.6Estimated · #29/189 | Not comparableBenchAlign lane · 0 vs 5 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 | 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) 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
- Claude Mythos Preview
- $0.0875
- Fit state unavailable
- GPT-6 Luna
- $0.00035
- Fits in one request
GPT-6 Luna has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Mythos Preview
- $1.63
- Fit state unavailable
- GPT-6 Luna
- $0.0065
- Fits in one request
GPT-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 Preview
- $6.75
- Fit state unavailable
- Cached input priced at the published list-input rate
- GPT-6 Luna
- $0.009
- Fits in one request
GPT-6 Luna has the lower modeled cost
Claude Mythos Preview has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationClaude Mythos PreviewGPT-6 Luna
Context window
Maximum documented context; output-token limits may be lower.
Claude Mythos Preview
Not sourced
Anthropic Project GlasswingGPT-6 Luna
API model ID
Claude Mythos Preview
claude-mythos-preview
Anthropic Project GlasswingGPT-6 Luna
gpt-6-luna
OpenAI GPT-6 Luna model documentationCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Mythos Preview
Not published
Anthropic Project GlasswingGPT-6 Luna
$0.01 per 1M cached input tokens
OpenAI GPT-6 Luna model documentationDocumented inputs
Claude Mythos Preview
Not sourced
GPT-6 Luna
text, image
OpenAI GPT-6 Luna model documentationDocumented outputs
Claude Mythos Preview
Not sourced
GPT-6 Luna
Provider availability
Claude Mythos Preview
Limited Access · Project Glasswing participants
Anthropic Project GlasswingGPT-6 Luna
Generally Available · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Work, Codex
OpenAI GPT-6 Sol and Luna launchReasoning profile
Claude Mythos Preview
Reasoning
GPT-6 Luna
Reasoning
Weight access
Claude Mythos Preview
Proprietary
GPT-6 Luna
Proprietary
License
Claude Mythos Preview
Proprietary
GPT-6 Luna
Proprietary
Release date
Claude Mythos Preview
2026-04-07
GPT-6 Luna
2026-09-16
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: $1.63 vs $0.0065. Cache-heavy agent loop: $6.75 vs $0.009.
- Context tradeoff
- A complete documented context comparison is not available.
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 evidence8 rows
Agentic
Coding
DeepSWE
Claude Mythos Preview—GPT-6 Luna66.6%SourceNot directly comparable
Knowledge
HealthBench (raw)
Claude Mythos Preview—GPT-6 Luna50.0%SourceNot directly comparable
HealthBench (length-adjusted)
Claude Mythos Preview—GPT-6 Luna54.5%SourceNot directly comparable
HealthBench Professional
Claude Mythos Preview—GPT-6 Luna60.8%SourceNot directly comparable
HealthBench Professional (raw)
Claude Mythos Preview—GPT-6 Luna61.2%SourceNot directly comparable
HealthBench Hard
Claude Mythos Preview—GPT-6 Luna31.4%SourceNot directly comparable
Questions
Which is better, Claude Mythos Preview or GPT-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 Preview or GPT-6 Luna?
Claude Mythos Preview is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Claude Mythos Preview or GPT-6 Luna?
Claude Mythos Preview is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Claude Mythos Preview or GPT-6 Luna?
For the stated presets, chat costs $0.0875 on Claude Mythos Preview and $0.00035 on GPT-6 Luna; repository review costs $1.63 and $0.0065; the cache-heavy agent loop costs $6.75 and $0.009. Claude Mythos Preview has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Claude Mythos Preview or GPT-6 Luna?
A complete documented context-window comparison is not available.
Related comparisons
Last updated September 22, 2026
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