Coding work
Code generation, repair, and software-engineering tasks
GPT-6 Astra
GPT-6 Astra leads on the public coding lane, 74.2 to 67, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
GPT-6.1 Sol vs GPT-6 Astra
Updated September 29, 2026. Rank says GPT-6 Astra is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty. This is a same-family comparison, so migration details appear when the source data supports them.
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
GPT-6 Astra has the higher public score, 88.22 versus 66.86, and the 90% score intervals do not overlap. 9 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 9
- GPT-6.1 Sol only
- 0
- GPT-6 Astra only
- 21
- Like-for-like categories
- 1 / 8
Estimated: GPT-6.1 Sol · Supported: GPT-6 AstraHow 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-6.1 Sol
GPT-6.1 Sol 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-6.1 Sol
GPT-6.1 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-6.1 Sol
GPT-6.1 Sol has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-6.1 Sol is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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.
67.0GPT-6.1 Sol74.2GPT-6 Astra
Like-for-like · BenchAlign v5.7
GPT-6 Astra leads the like-for-like coding row, although the 90% intervals overlap.
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 Shared
21 GPT-6 Astra 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.
AutomationBenchAgentic
Normalized gap 5.3GPT-6.1 Sol:36.1%GPT-6 Astra:41.4%
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.
Coding
Like-for-like- GPT-6.1 Sol
- 67.0
- Supported · #8/143
- GPT-6 Astra
- 74.2
- Supported · #4/143
- Basis
- BenchAlign v5.7 lane · 1 vs 4 public rows
- Reading
- GPT-6 Astra leads · intervals overlap
Knowledge
Directional only- GPT-6.1 Sol
- 71.3
- Estimated · #10/169
- GPT-6 Astra
- 86.2
- Supported · #2/169
- Basis
- BenchAlign v5.7 lane · 5 vs 7 public rows
- Reading
- Directional only
Agentic
Not comparable- GPT-6.1 Sol
- Not ranked
- GPT-6 Astra
- 70.7
- Supported · #5/117
- Basis
- BenchAlign v5.7 lane · 3 vs 10 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-6.1 Sol
- 79.4
- Unranked · 2 rankable rows
- GPT-6 Astra
- 89.6
- #1/27
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-6.1 Sol
- 83.9
- Unranked · 1 rankable row
- GPT-6 Astra
- 83.9
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-6.1 Sol
- Not ranked
- GPT-6 Astra
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-6.1 Sol
- Not ranked
- GPT-6 Astra
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-6.1 Sol
- Not ranked
- GPT-6 Astra
- 85.0
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
| Category | GPT-6.1 Sol | GPT-6 Astra | Basis | Reading |
|---|---|---|---|---|
| Coding | 67.0Supported · #8/143 | 74.2Supported · #4/143 | Like-for-likeBenchAlign v5.7 lane · 1 vs 4 public rows | GPT-6 Astra leads · intervals overlap |
| Knowledge | 71.3Estimated · #10/169 | 86.2Supported · #2/169 | Directional onlyBenchAlign v5.7 lane · 5 vs 7 public rows | Directional only |
| Agentic | Not ranked | 70.7Supported · #5/117 | Not comparableBenchAlign v5.7 lane · 3 vs 10 public rows | Not comparable |
| Reasoning | 79.4Unranked · 2 rankable rows | 89.6#1/27 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multimodal | 83.9Unranked · 1 rankable row | 83.9Unranked · 3 rankable rows | Not comparableProvisional lane · 0 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 | Not ranked | 85.0Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 1 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-6.1 Sol
- $0.007
- Fits in one request
- GPT-6 Astra
- $0.035
- Fits in one request
GPT-6.1 Sol has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-6.1 Sol
- $0.13
- Fits in one request
- GPT-6 Astra
- $0.65
- Fits in one request
GPT-6.1 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-6.1 Sol
- $0.16
- Fits in one request
- GPT-6 Astra
- $0.9
- Fits in one request
GPT-6.1 Sol 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-6.1 SolGPT-6 Astra
Context window
Maximum documented context; output-token limits may be lower.
GPT-6.1 Sol
GPT-6 Astra
API model ID
GPT-6.1 Sol
gpt-6.1-sol
OpenAI GPT-6.1 Sol model documentationGPT-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-6.1 Sol
$0.1 per 1M cached input tokens
OpenAI GPT-6.1 Sol model documentationGPT-6 Astra
$1 per 1M cached input tokens
OpenAI GPT-6 Astra model documentationDocumented inputs
GPT-6.1 Sol
text, image
OpenAI GPT-6.1 Sol model documentationGPT-6 Astra
text, image
OpenAI GPT-6 Astra model documentationDocumented outputs
GPT-6.1 Sol
GPT-6 Astra
Provider availability
GPT-6.1 Sol
Generally Available · OpenAI Responses API, OpenAI Chat Completions API
OpenAI GPT-6.1 Sol model documentationGPT-6 Astra
Limited Availability · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Plus, Pro, Business, and Enterprise, Microsoft Azure, AWS Bedrock
OpenAI GPT-6 Astra model documentationReasoning profile
GPT-6.1 Sol
Reasoning
GPT-6 Astra
Reasoning
Weight access
GPT-6.1 Sol
Proprietary
GPT-6 Astra
Proprietary
License
GPT-6.1 Sol
Proprietary
GPT-6 Astra
Proprietary
Release date
GPT-6.1 Sol
2026-09-29
GPT-6 Astra
2026-09-03
If you are choosing between sibling variants
- 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, 88.22 versus 66.86, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.13 vs $0.65. Cache-heavy agent loop: $0.16 vs $0.9.
- Context tradeoff
- Both models list 1.05M.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-6.1 Sol or GPT-6 Astra?
GPT-6 Astra has the higher public score, 88.22 versus 66.86, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, GPT-6.1 Sol or GPT-6 Astra?
GPT-6 Astra leads the public coding lane, 74.2 to 67, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, GPT-6.1 Sol or GPT-6 Astra?
GPT-6.1 Sol is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-6.1 Sol or GPT-6 Astra?
For the stated presets, chat costs $0.007 on GPT-6.1 Sol and $0.035 on GPT-6 Astra; repository review costs $0.13 and $0.65; the cache-heavy agent loop costs $0.16 and $0.9. Costs use the listed standard API rates.
Which has the larger context window, GPT-6.1 Sol or GPT-6 Astra?
Both models list the same context window, 1.05M.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence30 rows
Agentic
AutomationBench
GPT-6.1 Sol36.1%SourceGPT-6 Astra41.4%SourceGPT-6 Astra leads this result
Terminal-Bench-Science 0.1
GPT-6.1 Sol57.0%SourceGPT-6 Astra64.6%SourceGPT-6 Astra leads this result
ExploitGym
GPT-6.1 Sol35.1%SourceGPT-6 Astra42.4%SourceGPT-6 Astra leads this result
BrowseComp
GPT-6.1 Sol—GPT-6 Astra91.5%SourceNot directly comparable
OSWorld 2.0
GPT-6.1 Sol—GPT-6 Astra72.6%SourceNot directly comparable
Terminal-Bench 4.0
GPT-6.1 Sol—GPT-6 Astra57.90%SourceNot directly comparable
Agents' Last Exam
GPT-6.1 Sol—GPT-6 Astra59.3%SourceNot directly comparable
HLE w/ tools
GPT-6.1 Sol—GPT-6 Astra57.2%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
GPT-6.1 Sol—GPT-6 Astra87.3%SourceNot directly comparable
ApprenticeBench
GPT-6.1 Sol—GPT-6 Astra68%SourceNot directly comparable
Coding
DeepSWE
GPT-6.1 Sol71.9%SourceGPT-6 Astra74.1%SourceGPT-6 Astra leads this result
FrontierCode 1.1 Main
GPT-6.1 Sol—GPT-6 Astra53.3%SourceNot directly comparable
FrontierCode 1.1 Extended
GPT-6.1 Sol—GPT-6 Astra64.5%SourceNot directly comparable
FrontierSWE v2
GPT-6.1 Sol—GPT-6 Astra65.5%SourceNot directly comparable
Reasoning
ARC-AGI-1
GPT-6.1 Sol—GPT-6 Astra98.50%SourceNot directly comparable
ARC-AGI-2
GPT-6.1 Sol—GPT-6 Astra95%SourceNot directly comparable
ARC-AGI-3
GPT-6.1 Sol—GPT-6 Astra62.7%SourceNot directly comparable
GeneBench-Pro
GPT-6.1 Sol—GPT-6 Astra37.8%SourceNot directly comparable
MRCR v2 256K-512K
GPT-6.1 Sol—GPT-6 Astra100.0%SourceNot directly comparable
MRCR v2 512K-1M
GPT-6.1 Sol—GPT-6 Astra96.3%SourceNot directly comparable
Multimodal
Knowledge
HealthBench (raw)
GPT-6.1 Sol56.7%SourceGPT-6 Astra56.9%SourceGPT-6 Astra leads this result
HealthBench (length-adjusted)
GPT-6.1 Sol58.5%SourceGPT-6 Astra58.3%SourceGPT-6.1 Sol leads this result
HealthBench Professional
GPT-6.1 Sol64.2%SourceGPT-6 Astra64.7%SourceGPT-6 Astra leads this result
HealthBench Professional (raw)
GPT-6.1 Sol67.2%SourceGPT-6 Astra68.2%SourceGPT-6 Astra leads this result
HealthBench Hard
GPT-6.1 Sol36.2%SourceGPT-6 Astra36.6%SourceGPT-6 Astra leads this result
GPQA
GPT-6.1 Sol—GPT-6 Astra96%SourceNot directly comparable
GPQA-D
GPT-6.1 Sol—GPT-6 Astra96.0%SourceNot directly comparable
Math
FrontierMath v2 (Tier 4)
GPT-6.1 Sol—GPT-6 Astra97.600%SourceNot directly comparable
30 public results · 9 shared
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Last updated September 29, 2026