Reasoning
Like-for-like- GPT-5.6 Sol
- 92.5
- GPT-6 Astra
- 95.0
- Weighted basis
- 1 vs 1 rows
- Reading
- GPT-6 Astra leads
GPT-5.6 Sol vs GPT-6 Astra
Updated September 3, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GPT-6 Astra has the higher public score estimate, 81.88 versus 81.78, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
10 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. 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 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
No clear pick
The documented context windows are equal.
Confidence: documented
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
20 GPT-5.6 Sol only
10 Shared
6 GPT-6 Astra only
- Shared results
- 10
- GPT-5.6 Sol only
- 20
- GPT-6 Astra only
- 6
- Like-for-like categories
- 2 / 8
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Knowledge
Like-for-like- GPT-5.6 Sol
- 94.6
- GPT-6 Astra
- 96.0
- Weighted basis
- 1 vs 1 rows
- Reading
- GPT-6 Astra leads
Math
Directional only- GPT-5.6 Sol
- 87.5
- GPT-6 Astra
- 97.6
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Agentic
Not comparable- GPT-5.6 Sol
- 92.0
- GPT-6 Astra
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Coding
Not comparable- GPT-5.6 Sol
- 64.6
- GPT-6 Astra
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.6 Sol
- Not measured
- GPT-6 Astra
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.6 Sol
- 83.0
- GPT-6 Astra
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.6 Sol
- Not measured
- GPT-6 Astra
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GPT-5.6 Sol | GPT-6 Astra | Weighted basis | Reading |
|---|---|---|---|---|
| Reasoning | 92.5 | 95.0 | Like-for-like1 vs 1 rows | GPT-6 Astra leads |
| Knowledge | 94.6 | 96.0 | Like-for-like1 vs 1 rows | GPT-6 Astra leads |
| Math | 87.5 | 97.6 | Directional only2 vs 1 rows | Directional only |
| Agentic | 92.0 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Coding | 64.6 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | 83.0 | Not measured | Not comparable1 vs 0 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.
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
ARC-AGI-2
Reasoning
GPQA
Knowledge
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 Sol
- $0.02
- Fits in one request
- GPT-6 Astra
- $0.035
- 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
- GPT-5.6 Sol
- $0.34
- Fits in one request
- GPT-6 Astra
- $0.65
- 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
- GPT-5.6 Sol
- $0.5
- Fits in one request
- GPT-6 Astra
- $0.9
- Fits in one request
GPT-5.6 Sol has the lower modeled cost
Costs use the listed standard API rates.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.6 SolGPT-6 Astra
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.6 Sol
1.05M
OpenAI model catalogGPT-6 Astra
API model ID
GPT-5.6 Sol
gpt-5.6-sol
OpenAI model catalogGPT-6 Astra
gpt-6-astra
OpenAI GPT-6 Astra model documentationCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.6 Sol
$0.5 per 1M cached input tokens
OpenAI pricingGPT-6 Astra
$1 per 1M cached input tokens
OpenAI GPT-6 Astra model documentationDocumented inputs
GPT-5.6 Sol
text, image
OpenAI model catalogGPT-6 Astra
text, image
OpenAI GPT-6 Astra model documentationDocumented outputs
GPT-5.6 Sol
GPT-6 Astra
Provider availability
GPT-5.6 Sol
Generally Available · OpenAI Responses API
OpenAI model catalogGPT-6 Astra
Limited Availability · OpenAI Responses API, ChatGPT Plus, Pro, Business, and Enterprise
OpenAI GPT-6 Astra model documentationReasoning profile
GPT-5.6 Sol
Reasoning
GPT-6 Astra
Reasoning
Weight access
GPT-5.6 Sol
Proprietary
GPT-6 Astra
Proprietary
License
GPT-5.6 Sol
Proprietary
GPT-6 Astra
Proprietary
Release date
GPT-5.6 Sol
2026-07-09
GPT-6 Astra
2026-09-03
If you are considering the documented upgrade path
- Deployment change
- Both entries list OpenAI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- GPT-6 Astra has the higher public score estimate, 81.88 versus 81.78, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.34 vs $0.65. Cache-heavy agent loop: $0.5 vs $0.9.
- Context tradeoff
- Both models list 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 evidence36 rows
Agentic
Terminal-Bench 3.0
GPT-5.6 Sol34.6%SourceGPT-6 Astra—Not directly comparable
Terminal-Bench 2.0
GPT-5.6 Sol91.9%SourceGPT-6 Astra—Not directly comparable
BrowseComp
GPT-5.6 Sol92.2%SourceGPT-6 Astra—Not directly comparable
OSWorld 2.0
GPT-5.6 Sol62.6%SourceGPT-6 Astra72.6%SourceGPT-6 Astra leads this result
CyberGym
GPT-5.6 Sol84.5%SourceGPT-6 Astra—Not directly comparable
ExploitGym
GPT-5.6 Sol33.7%SourceGPT-6 Astra42.4%SourceGPT-6 Astra leads this result
Toolathlon
GPT-5.6 Sol58%SourceGPT-6 Astra—Not directly comparable
Terminal-Bench 4.0
GPT-5.6 Sol—GPT-6 Astra57.70%SourceNot directly comparable
Terminal-Bench-Science 0.1
GPT-5.6 Sol—GPT-6 Astra64.6%SourceNot directly comparable
Agents' Last Exam
GPT-5.6 Sol—GPT-6 Astra59.3%SourceNot directly comparable
Coding
Bug Hunt Bench
GPT-5.6 Sol42 fixesSourceGPT-6 Astra—Not directly comparable
SWE-bench Pro
GPT-5.6 Sol64.6%SourceGPT-6 Astra—Not directly comparable
Terminal-Bench 2.0
GPT-5.6 Sol91.9%SourceGPT-6 Astra—Not directly comparable
deepSwe
GPT-5.6 Sol72.7%SourceGPT-6 Astra74.1%SourceGPT-6 Astra leads this result
FrontierCode 1.1 Extended
GPT-5.6 Sol60.6%SourceGPT-6 Astra—Not directly comparable
FrontierSWE v2
GPT-5.6 Sol32.2%SourceGPT-6 Astra—Not directly comparable
cursorBench32
GPT-5.6 Sol67.2%SourceGPT-6 Astra—Not directly comparable
VulcanBench v3
GPT-5.6 Sol87.0%SourceGPT-6 Astra—Not directly comparable
VulcanBench CII v1
GPT-5.6 Sol86.5%SourceGPT-6 Astra—Not directly comparable
Reasoning
ARC-AGI-2
GPT-5.6 Sol92.5%SourceGPT-6 Astra95%SourceGPT-6 Astra leads this result
ARC-AGI-3
GPT-5.6 Sol7.8%SourceGPT-6 Astra62.7%SourceGPT-6 Astra leads this result
GeneBench-Pro
GPT-5.6 Sol28.7%SourceGPT-6 Astra—Not directly comparable
MRCR v2 256K-512K
GPT-5.6 Sol—GPT-6 Astra100.0%SourceNot directly comparable
MRCR v2 512K-1M
GPT-5.6 Sol—GPT-6 Astra96.3%SourceNot directly comparable
Knowledge
GPQA
GPT-5.6 Sol94.6%SourceGPT-6 Astra96%SourceGPT-6 Astra leads this result
GPQA-D
GPT-5.6 Sol94.6%SourceGPT-6 Astra96.0%SourceGPT-6 Astra leads this result
HLE-Verified
GPT-5.6 Sol54.5%SourceGPT-6 Astra—Not directly comparable
LABBench2
GPT-5.6 Sol82.1%SourceGPT-6 Astra—Not directly comparable
HealthBench Professional
GPT-5.6 Sol60.5%SourceGPT-6 Astra63.4%SourceGPT-6 Astra leads this result
HealthBench Hard
GPT-5.6 Sol33.1%SourceGPT-6 Astra36.3%SourceGPT-6 Astra leads this result
Math
Multimodal
Frequently asked questions
Which is better, GPT-5.6 Sol or GPT-6 Astra?
GPT-6 Astra has the higher public score estimate, 81.88 versus 81.78, 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 Sol or GPT-6 Astra?
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, GPT-5.6 Sol or GPT-6 Astra?
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, GPT-5.6 Sol or GPT-6 Astra?
For the stated presets, chat costs $0.02 on GPT-5.6 Sol and $0.035 on GPT-6 Astra; repository review costs $0.34 and $0.65; the cache-heavy agent loop costs $0.5 and $0.9. Costs use the listed standard API rates.
Which has the larger context window, GPT-5.6 Sol or GPT-6 Astra?
Both models list the same context window, 1.05M.
Related comparisons
Last updated September 3, 2026
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