Agentic
Not comparable- Claude Mythos Preview
- Not measured
- GPT-5.6 Sol
- 92.0
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Claude Mythos Preview vs GPT-5.6 Sol
Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
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 based on different benchmark sets are marked directional and do not name a winner.
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 Sol
GPT-5.6 Sol 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-5.6 Sol
GPT-5.6 Sol 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-5.6 Sol
GPT-5.6 Sol 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
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Not enough matched evidence
A complete context comparison is not sourced.
Confidence: limited
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 Shared
30 GPT-5.6 Sol only
- Shared results
- 2
- Claude Mythos Preview only
- 0
- GPT-5.6 Sol only
- 30
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Coding
Not comparable- Claude Mythos Preview
- Not measured
- GPT-5.6 Sol
- 64.6
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Reasoning
Not comparable- Claude Mythos Preview
- Not measured
- GPT-5.6 Sol
- 92.5
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Knowledge
Not comparable- Claude Mythos Preview
- Not measured
- GPT-5.6 Sol
- 94.6
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Math
Not comparable- Claude Mythos Preview
- Not measured
- GPT-5.6 Sol
- 87.5
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Mythos Preview
- Not measured
- GPT-5.6 Sol
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Mythos Preview
- Not measured
- GPT-5.6 Sol
- 83.0
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Mythos Preview
- Not measured
- GPT-5.6 Sol
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Claude Mythos Preview | GPT-5.6 Sol | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | Not measured | 92.0 | Not comparable0 vs 2 rows | Not comparable |
| Coding | Not measured | 64.6 | Not comparable0 vs 1 rows | Not comparable |
| Reasoning | Not measured | 92.5 | Not comparable0 vs 1 rows | Not comparable |
| Knowledge | Not measured | 94.6 | Not comparable0 vs 1 rows | Not comparable |
| Math | Not measured | 87.5 | Not comparable0 vs 2 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 83.0 | Not comparable0 vs 1 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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-5.6 Sol
- $0.02
- Fits in one request
GPT-5.6 Sol 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-5.6 Sol
- $0.34
- Fits in one request
GPT-5.6 Sol 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-5.6 Sol
- $0.5
- Fits in one request
GPT-5.6 Sol 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-5.6 Sol
Context window
Maximum documented context; output-token limits may be lower.
Claude Mythos Preview
Not sourced
GPT-5.6 Sol
1.05M
OpenAI model catalogAPI model ID
Claude Mythos Preview
claude-mythos-preview
Anthropic Project GlasswingGPT-5.6 Sol
gpt-5.6-sol
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 Preview
Not published
Anthropic Project GlasswingGPT-5.6 Sol
$0.5 per 1M cached input tokens
OpenAI pricingDocumented inputs
Claude Mythos Preview
Not sourced
GPT-5.6 Sol
text, image
OpenAI model catalogDocumented outputs
Claude Mythos Preview
Not sourced
GPT-5.6 Sol
Provider availability
Claude Mythos Preview
Not sourced
GPT-5.6 Sol
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
Claude Mythos Preview
Reasoning
GPT-5.6 Sol
Reasoning
Weight access
Claude Mythos Preview
Proprietary
GPT-5.6 Sol
Proprietary
License
Claude Mythos Preview
Proprietary
GPT-5.6 Sol
Proprietary
Release date
Claude Mythos Preview
2026-04-07
GPT-5.6 Sol
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: $1.63 vs $0.34. Cache-heavy agent loop: $6.75 vs $0.5.
- 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 evidence32 rows
Agentic
CyberGym
Claude Mythos Preview83.1%SourceGPT-5.6 Sol84.5%SourceGPT-5.6 Sol leads this result
ExploitGym
Claude Mythos Preview17.5%SourceGPT-5.6 Sol33.7%SourceGPT-5.6 Sol leads this result
Terminal-Bench 3.0
Claude Mythos Preview—GPT-5.6 Sol34.6%SourceNot directly comparable
Terminal-Bench 2.0
Claude Mythos Preview—GPT-5.6 Sol91.9%SourceNot directly comparable
BrowseComp
Claude Mythos Preview—GPT-5.6 Sol92.2%SourceNot directly comparable
OSWorld 2.0
Claude Mythos Preview—GPT-5.6 Sol62.6%SourceNot directly comparable
Toolathlon
Claude Mythos Preview—GPT-5.6 Sol58%SourceNot directly comparable
Coding
SWE-bench Pro
Claude Mythos Preview—GPT-5.6 Sol64.6%SourceNot directly comparable
Terminal-Bench 2.0
Claude Mythos Preview—GPT-5.6 Sol91.9%SourceNot directly comparable
deepSwe
Claude Mythos Preview—GPT-5.6 Sol72.7%SourceNot directly comparable
FrontierCode 1.1 Extended
Claude Mythos Preview—GPT-5.6 Sol60.6%SourceNot directly comparable
cursorBench32
Claude Mythos Preview—GPT-5.6 Sol67.2%SourceNot directly comparable
VulcanBench v3
Claude Mythos Preview—GPT-5.6 Sol87.0%SourceNot directly comparable
APEX-SWE
Claude Mythos Preview—GPT-5.6 Sol45.8%SourceNot directly comparable
EEBench
Claude Mythos Preview—GPT-5.6 Sol39.4%SourceNot directly comparable
CADGenBench Generation
Claude Mythos Preview—GPT-5.6 Sol37.1%SourceNot directly comparable
CADBench
Claude Mythos Preview—GPT-5.6 Sol86.5%SourceNot directly comparable
SpaceXAI MTS Eval
Claude Mythos Preview—GPT-5.6 Sol52.1%SourceNot directly comparable
InferenceEval
Claude Mythos Preview—GPT-5.6 Sol44.1%SourceNot directly comparable
KernelBench Internal
Claude Mythos Preview—GPT-5.6 Sol73.1%SourceNot directly comparable
Reasoning
Knowledge
GPQA
Claude Mythos Preview—GPT-5.6 Sol94.6%SourceNot directly comparable
GPQA-D
Claude Mythos Preview—GPT-5.6 Sol94.6%SourceNot directly comparable
HealthBench Professional
Claude Mythos Preview—GPT-5.6 Sol60.5%SourceNot directly comparable
HealthBench Hard
Claude Mythos Preview—GPT-5.6 Sol33.1%SourceNot directly comparable
Math
Multimodal
Frequently asked questions
Which is better, Claude Mythos Preview or GPT-5.6 Sol?
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-5.6 Sol?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, Claude Mythos Preview or GPT-5.6 Sol?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, Claude Mythos Preview or GPT-5.6 Sol?
For the stated presets, chat costs $0.0875 on Claude Mythos Preview and $0.02 on GPT-5.6 Sol; repository review costs $1.63 and $0.34; the cache-heavy agent loop costs $6.75 and $0.5. 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-5.6 Sol?
A complete documented context-window comparison is not available.
Related comparisons
Last updated August 13, 2026
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