Knowledge
Directional only- GPT-6 Astra
- 96.0
- Kimi K2.6
- 42.2
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Five or fewer confirmed AI changes, with original sources, on mornings when something changed.A free source-linked morning brief.
See the free Radar BriefUpdated 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, 81.88 versus 58.91, and the 90% score intervals do not overlap.
4 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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.
Prompts that approach the documented context limit
GPT-6 Astra
GPT-6 Astra has the larger documented context window.
Confidence: documented
1K fresh input + 500 output tokens
Kimi K2.6
Kimi K2.6 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
200K cached + 20K fresh input + 10K output tokens
Kimi K2.6
Kimi K2.6 has the lower estimated token cost for this stated workload. Kimi K2.6 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
50K fresh input + 3K output tokens
Kimi K2.6
Kimi K2.6 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
| Category | GPT-6 Astra | Kimi K2.6 | Weighted basis | Reading |
|---|---|---|---|---|
| Knowledge | 96.0 | 42.2 | Directional only1 vs 2 rows | Directional only |
| Math | 97.6 | 67.1 | Directional only1 vs 4 rows | Directional only |
| Agentic | Not measured | 73.5 | Not comparable0 vs 3 rows | Not comparable |
| Coding | Not measured | 64.4 | Not comparable0 vs 3 rows | Not comparable |
| Reasoning | 95.0 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 79.8 | Not comparable0 vs 2 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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
GPQA
Knowledge
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.
1K fresh input + 500 output tokens
Kimi K2.6 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Kimi K2.6 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Kimi K2.6 has the lower modeled cost
Kimi K2.6 has no published cached-input rate, so cached tokens use its listed input rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
GPT-6 Astra
Kimi K2.6
256K
GPT-6 Astra
gpt-6-astra
OpenAI GPT-6 Astra model documentationKimi K2.6
Not sourced
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 documentationKimi K2.6
Not published
GPT-6 Astra
text, image
OpenAI GPT-6 Astra model documentationKimi K2.6
Not sourced
GPT-6 Astra
Kimi K2.6
Not sourced
GPT-6 Astra
Limited Availability · OpenAI Responses API, ChatGPT Plus, Pro, Business, and Enterprise
OpenAI GPT-6 Astra model documentationKimi K2.6
Not sourced
GPT-6 Astra
Reasoning
Kimi K2.6
Reasoning
GPT-6 Astra
Proprietary
Kimi K2.6
Open Weight
GPT-6 Astra
Proprietary
Kimi K2.6
Open Weight
GPT-6 Astra
2026-09-03
Kimi K2.6
2026-04-20
Run the same representative tasks against both endpoints before changing production traffic.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
OSWorld 2.0
GPT-6 Astra leads this result
Terminal-Bench 4.0
Not directly comparable
Terminal-Bench-Science 0.1
Not directly comparable
ExploitGym
Not directly comparable
Agents' Last Exam
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
BrowseComp
Not directly comparable
OSWorld-Verified
Not directly comparable
Toolathlon
Not directly comparable
MCP Atlas
Not directly comparable
Claw-Eval
Not directly comparable
DeepSearchQA
Not directly comparable
WideResearch
Not directly comparable
Gert Labs
Not directly comparable
ResearchClawBench
Not directly comparable
deepSwe
Not directly comparable
SWE-bench Verified
Not directly comparable
LiveCodeBench v6
Not directly comparable
SWE-bench Pro
Not directly comparable
SWE Multilingual
Not directly comparable
SciCode
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Not directly comparable
cursorBench31
Not directly comparable
GPQA
GPT-6 Astra leads this result
GPQA-D
GPT-6 Astra leads this result
HealthBench Professional
Not directly comparable
HealthBench Hard
Not directly comparable
HLE
Not directly comparable
FrontierMath v2 (Tier 4)
GPT-6 Astra leads this result
AIME26
Not directly comparable
HMMT Feb 2026
Not directly comparable
MMAnswerBench
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Not directly comparable
ScreenSpot Pro
Not directly comparable
MMMU-Pro
Not directly comparable
MMMU-Pro w/ Python
Not directly comparable
CharXiv
Not directly comparable
MathVision
Not directly comparable
V*
Not directly comparable
GPT-6 Astra has the higher public score, 81.88 versus 58.91, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
For the stated presets, chat costs $0.035 on GPT-6 Astra and $0.00295 on Kimi K2.6; repository review costs $0.65 and $0.0595; the cache-heavy agent loop costs $0.9 and $0.249. Kimi K2.6 has no published cached-input rate, so cached tokens use its listed input rate.
GPT-6 Astra has the larger documented context window: 1.05M, compared with 256K.
Last updated September 3, 2026
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