Agentic
Not comparable- GPT-6 Astra
- 70.4
- Supported · #5/152
- SWE-2
- Not ranked
- Basis
- BenchAlign lane · 9 vs 2 public rows
- Reading
- Not comparable
GPT-6 Astra vs SWE-2
Updated September 10, 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.
3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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.
Long documents
Prompts that approach the documented context limit
GPT-6 Astra
GPT-6 Astra has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
SWE-2 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
SWE-2 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
22 GPT-6 Astra only
3 Shared
2 SWE-2 only
- Shared results
- 3
- GPT-6 Astra only
- 22
- SWE-2 only
- 2
- 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- GPT-6 Astra
- 74.6
- Supported · #4/151
- SWE-2
- Not ranked
- Basis
- BenchAlign lane · 3 vs 3 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-6 Astra
- 89.5
- #1/20
- SWE-2
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-6 Astra
- 82.2
- Supported · #4/183
- SWE-2
- Not ranked
- Basis
- BenchAlign lane · 4 vs 0 public rows
- Reading
- Not comparable
Math
Not comparable- GPT-6 Astra
- 85.2
- Unranked · 1 rankable row
- SWE-2
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-6 Astra
- Not ranked
- SWE-2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-6 Astra
- 82.8
- Unranked · 3 rankable rows
- SWE-2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-6 Astra
- Not ranked
- SWE-2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-6 Astra | SWE-2 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 70.4Supported · #5/152 | Not ranked | Not comparableBenchAlign lane · 9 vs 2 public rows | Not comparable |
| Coding | 74.6Supported · #4/151 | Not ranked | Not comparableBenchAlign lane · 3 vs 3 public rows | Not comparable |
| Reasoning | 89.5#1/20 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Knowledge | 82.2Supported · #4/183 | Not ranked | Not comparableBenchAlign lane · 4 vs 0 public rows | Not comparable |
| Math | 85.2Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 82.8Unranked · 3 rankable rows | 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 |
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
- GPT-6 Astra
- $0.035
- Fits in one request
- SWE-2
- API rate not published
- Fits in one request
SWE-2 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GPT-6 Astra
- $0.65
- Fits in one request
- SWE-2
- API rate not published
- Fits in one request
SWE-2 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-6 Astra
- $0.9
- Fits in one request
- SWE-2
- API rate not published
- Fits in one request
- Cached-input rate unavailable
SWE-2 has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-6 AstraSWE-2
Context window
Maximum documented context; output-token limits may be lower.
GPT-6 Astra
SWE-2
API model ID
GPT-6 Astra
gpt-6-astra
OpenAI GPT-6 Astra model documentationSWE-2
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-6 Astra
$1 per 1M cached input tokens
OpenAI GPT-6 Astra model documentationSWE-2
No comparable hosted API rate
Cognition SWE-2 launch postDocumented inputs
GPT-6 Astra
text, image
OpenAI GPT-6 Astra model documentationSWE-2
Not sourced
Documented outputs
GPT-6 Astra
SWE-2
Not sourced
Provider availability
GPT-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 documentationSWE-2
Not sourced
Reasoning profile
GPT-6 Astra
Reasoning
SWE-2
Reasoning
Weight access
GPT-6 Astra
Proprietary
SWE-2
Proprietary
License
GPT-6 Astra
Proprietary
SWE-2
Proprietary
Release date
GPT-6 Astra
2026-09-03
SWE-2
2026-09-10
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
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- GPT-6 Astra has the larger documented window (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 evidence27 rows
Agentic
BrowseComp
GPT-6 Astra91.5%SourceSWE-2—Not directly comparable
OSWorld 2.0
GPT-6 Astra72.6%SourceSWE-2—Not directly comparable
Terminal-Bench 4.0
GPT-6 Astra57.90%SourceSWE-227.30%SourceGPT-6 Astra leads this result
Terminal-Bench-Science 0.1
GPT-6 Astra64.6%SourceSWE-2—Not directly comparable
ExploitGym
GPT-6 Astra42.4%SourceSWE-2—Not directly comparable
Agents' Last Exam
GPT-6 Astra59.3%SourceSWE-2—Not directly comparable
AutomationBench
GPT-6 Astra41.4%SourceSWE-2—Not directly comparable
HLE w/ tools
GPT-6 Astra57.2%SourceSWE-2—Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-6 Astra87.3%SourceSWE-2—Not directly comparable
Terminal-Bench 2.1
GPT-6 Astra—SWE-292.8%SourceNot directly comparable
Coding
DeepSWE
GPT-6 Astra74.1%SourceSWE-273.0%SourceGPT-6 Astra leads this result
FrontierCode 1.1 Main
GPT-6 Astra53.3%SourceSWE-250.0%SourceGPT-6 Astra leads this result
FrontierCode 1.1 Extended
GPT-6 Astra64.5%SourceSWE-2—Not directly comparable
Terminal-Bench 2.1
GPT-6 Astra—SWE-292.8%SourceNot directly comparable
Reasoning
ARC-AGI-1
GPT-6 Astra98.50%SourceSWE-2—Not directly comparable
ARC-AGI-2
GPT-6 Astra95%SourceSWE-2—Not directly comparable
ARC-AGI-3
GPT-6 Astra62.7%SourceSWE-2—Not directly comparable
GeneBench-Pro
GPT-6 Astra37.8%SourceSWE-2—Not directly comparable
MRCR v2 256K-512K
GPT-6 Astra100.0%SourceSWE-2—Not directly comparable
MRCR v2 512K-1M
GPT-6 Astra96.3%SourceSWE-2—Not directly comparable
Knowledge
Math
FrontierMath v2 (Tier 4)
GPT-6 Astra97.600%SourceSWE-2—Not directly comparable
Multimodal
Frequently asked questions
Which is better, GPT-6 Astra or SWE-2?
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, GPT-6 Astra or SWE-2?
SWE-2 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GPT-6 Astra or SWE-2?
SWE-2 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-6 Astra or SWE-2?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, GPT-6 Astra or SWE-2?
GPT-6 Astra has the larger documented context window: 1.05M, compared with 1M.
Related comparisons
Last updated September 10, 2026
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