Agentic
Like-for-like- GPT-5.5
- 61.1
- Supported · #17/153
- Kimi K2.6
- 46.6
- Supported · #77/153
- Basis
- BenchAlign lane · 14 vs 12 public rows
- Reading
- GPT-5.5 leads
GPT-5.5 vs Kimi K2.6
Updated September 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GPT-5.5 has the higher public score estimate, 72.14 versus 65.46, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
24 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.
Coding work
Code generation, repair, and software-engineering tasks
GPT-5.5
GPT-5.5 leads on the public coding lane, 67.6 to 50.8, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Agentic work
Tool use, computer use, and multi-step task completion
GPT-5.5
GPT-5.5 leads on the public agentic lane, 61.1 to 46.6, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Long documents
Prompts that approach the documented context limit
GPT-5.5
GPT-5.5 has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Chat turn cost
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
Cache-heavy agent loop cost
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
Repository review cost
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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
15 GPT-5.5 only
24 Shared
13 Kimi K2.6 only
- Shared results
- 24
- GPT-5.5 only
- 15
- Kimi K2.6 only
- 13
- Like-for-like categories
- 3 / 8
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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
Like-for-like- GPT-5.5
- 67.6
- Supported · #7/152
- Kimi K2.6
- 50.8
- Supported · #53/152
- Basis
- BenchAlign lane · 9 vs 10 public rows
- Reading
- GPT-5.5 leads
Knowledge
Like-for-like- GPT-5.5
- 72.9
- Supported · #7/183
- Kimi K2.6
- 61.4
- Supported · #32/183
- Basis
- BenchAlign lane · 6 vs 5 public rows
- Reading
- GPT-5.5 leads · intervals overlap
Multimodal
Directional only- GPT-5.5
- 71.3
- #19/48
- Kimi K2.6
- 64.0
- #26/48
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.5
- 64.0
- #13/20
- Kimi K2.6
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.5
- 69.6
- Unranked · 3 rankable rows
- Kimi K2.6
- 71.3
- #1/7
- Basis
- Provisional lane · 2 vs 4 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.5
- Not ranked
- Kimi K2.6
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.5
- 93.2
- #7/123
- Kimi K2.6
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.5 | Kimi K2.6 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 61.1Supported · #17/153 | 46.6Supported · #77/153 | Like-for-likeBenchAlign lane · 14 vs 12 public rows | GPT-5.5 leads |
| Coding | 67.6Supported · #7/152 | 50.8Supported · #53/152 | Like-for-likeBenchAlign lane · 9 vs 10 public rows | GPT-5.5 leads |
| Knowledge | 72.9Supported · #7/183 | 61.4Supported · #32/183 | Like-for-likeBenchAlign lane · 6 vs 5 public rows | GPT-5.5 leads · intervals overlap |
| Multimodal | 71.3#19/48 | 64.0#26/48 | Directional onlyProvisional lane · 2 vs 2 weighted rows | Directional only |
| Reasoning | 64.0#13/20 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Math | 69.6Unranked · 3 rankable rows | 71.3#1/7 | Not comparableProvisional lane · 2 vs 4 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 93.2#7/123 | 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.
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
HLE
Knowledge
Terminal-Bench 2.0
Agentic
FrontierMath v2 (Tiers 1-3)
Math
OSWorld 2.0
Agentic
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-5.5
- $0.02
- Fits in one request
- Kimi K2.6
- $0.00295
- Fits in one request
Kimi K2.6 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-5.5
- $0.34
- Fits in one request
- Kimi K2.6
- $0.0595
- Fits in one request
Kimi K2.6 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-5.5
- $0.5
- Fits in one request
- Kimi K2.6
- $0.249
- Fits in one request
- Cached input priced at the published list-input rate
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.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.5Kimi K2.6
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.5
Kimi K2.6
256K
API model ID
GPT-5.5
gpt-5.5
OpenAI pricingKimi K2.6
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.5
$0.5 per 1M cached input tokens
OpenAI pricingKimi K2.6
Not published
Documented inputs
GPT-5.5
Not sourced
Kimi K2.6
Not sourced
Documented outputs
GPT-5.5
Not sourced
Kimi K2.6
Not sourced
Provider availability
GPT-5.5
Not sourced
Kimi K2.6
Not sourced
Reasoning profile
GPT-5.5
Reasoning
Kimi K2.6
Reasoning
Weight access
GPT-5.5
Proprietary
Kimi K2.6
Open Weight
License
GPT-5.5
Proprietary
Kimi K2.6
Open Weight
Release date
GPT-5.5
2026-04-23
Kimi K2.6
2026-04-20
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
- GPT-5.5 has the higher public score estimate, 72.14 versus 65.46, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.34 vs $0.0595. Cache-heavy agent loop: $0.5 vs $0.249.
- Context tradeoff
- GPT-5.5 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
GPT-5.5
API / mo$26,250
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Kimi K2.6
API / mo$3,713
Self-host / mo$18,221
Break-even326M/day
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence52 rows
Agentic
Terminal-Bench 2.0
GPT-5.582%SourceKimi K2.666.7%SourceGPT-5.5 leads this result
CyberGym
GPT-5.581.8%SourceKimi K2.6—Not directly comparable
BrowseComp
GPT-5.584.4%SourceKimi K2.683.2%SourceGPT-5.5 leads this result
OSWorld-Verified
GPT-5.578.7%SourceKimi K2.673.1%SourceGPT-5.5 leads this result
MCP Atlas
GPT-5.575.3%SourceKimi K2.655.9%SourceGPT-5.5 leads this result
Toolathlon
GPT-5.555.6%SourceKimi K2.650%SourceGPT-5.5 leads this result
τ²-bench results
GPT-5.598%SourceKimi K2.6—Not directly comparable
Gert Labs
Shared sourceGPT-5.572.93%Kimi K2.656.82%GPT-5.5 leads this result
ResearchClawBench
Shared sourceGPT-5.517.0%Kimi K2.618.0%Kimi K2.6 leads this result
OSWorld 2.0
Shared sourceGPT-5.513.0%Kimi K2.64.6%GPT-5.5 leads this result
JobBench
GPT-5.542.7%SourceKimi K2.6—Not directly comparable
ExploitGym
GPT-5.513.4%SourceKimi K2.6—Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.576.4%SourceKimi K2.653.6%SourceGPT-5.5 leads this result
ApprenticeBench
GPT-5.520%SourceKimi K2.6—Not directly comparable
Claw-Eval
GPT-5.5—Kimi K2.662.3%SourceNot directly comparable
DeepSearchQA
GPT-5.5—Kimi K2.692.5%SourceNot directly comparable
WideResearch
GPT-5.5—Kimi K2.680.8%SourceNot directly comparable
Coding
SWE-bench Pro
GPT-5.558.6%SourceKimi K2.658.6%SourceTie
Terminal-Bench 2.0
GPT-5.582.0%SourceKimi K2.666.7%SourceGPT-5.5 leads this result
Vibe Code Bench
Shared sourceGPT-5.569.85%Kimi K2.637.89%GPT-5.5 leads this result
React Native Evals
GPT-5.584.7%SourceKimi K2.6—Not directly comparable
cursorBench31
Shared sourceGPT-5.559.2%Kimi K2.647.6%GPT-5.5 leads this result
cursorBench32
GPT-5.558.4%SourceKimi K2.6—Not directly comparable
FrontierCode 1.1 Main
GPT-5.543.0%SourceKimi K2.6—Not directly comparable
LiveCodeBench (Vals)
GPT-5.585.3%SourceKimi K2.686.8%SourceKimi K2.6 leads this result
SWE-bench (Vals)
GPT-5.582.6%SourceKimi K2.676.2%SourceGPT-5.5 leads this result
SWE-bench Verified
GPT-5.5—Kimi K2.680.2%SourceNot directly comparable
LiveCodeBench v6
GPT-5.5—Kimi K2.689.6%SourceNot directly comparable
SWE Multilingual
GPT-5.5—Kimi K2.676.7%SourceNot directly comparable
SciCode
GPT-5.5—Kimi K2.652.2%SourceNot directly comparable
Reasoning
Knowledge
GPQA
GPT-5.593.6%SourceKimi K2.690.5%SourceGPT-5.5 leads this result
GPQA-D
GPT-5.593.6%SourceKimi K2.690.5%SourceGPT-5.5 leads this result
HLE
GPT-5.552.2%SourceKimi K2.634.7%SourceGPT-5.5 leads this result
HLE w/o tools
GPT-5.541.4%SourceKimi K2.6—Not directly comparable
GPQA Diamond (Vals)
GPT-5.593.2%SourceKimi K2.689.1%SourceGPT-5.5 leads this result
MMLU-Pro (Vals)
GPT-5.588.1%SourceKimi K2.687.6%SourceGPT-5.5 leads this result
Math
FrontierMath (legacy)
GPT-5.551.7%SourceKimi K2.6—Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared sourceGPT-5.551.700%Kimi K2.638.966%GPT-5.5 leads this result
FrontierMath v2 (Tier 4)
Shared sourceGPT-5.535.400%Kimi K2.614.580%GPT-5.5 leads this result
AIME26
GPT-5.5—Kimi K2.696.4%SourceNot directly comparable
HMMT Feb 2026
GPT-5.5—Kimi K2.692.7%SourceNot directly comparable
MMAnswerBench
GPT-5.5—Kimi K2.686.0%SourceNot directly comparable
Multimodal
MMMU-Pro
GPT-5.581.2%SourceKimi K2.679.4%SourceGPT-5.5 leads this result
MMMU-Pro w/ Python
GPT-5.583.2%SourceKimi K2.680.1%SourceGPT-5.5 leads this result
OfficeQA Pro
GPT-5.554.1%SourceKimi K2.6—Not directly comparable
CharXiv
GPT-5.5—Kimi K2.680.4%SourceNot directly comparable
MathVision
GPT-5.5—Kimi K2.687.4%SourceNot directly comparable
V*
GPT-5.5—Kimi K2.696.9%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-5.5 or Kimi K2.6?
GPT-5.5 has the higher public score estimate, 72.14 versus 65.46, 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-5.5 or Kimi K2.6?
GPT-5.5 leads the public coding lane, 67.6 to 50.8, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, GPT-5.5 or Kimi K2.6?
GPT-5.5 leads the public agentic tasks lane, 61.1 to 46.6, with Supported evidence for both models and non-overlapping 90% intervals.
Which costs less, GPT-5.5 or Kimi K2.6?
For the stated presets, chat costs $0.02 on GPT-5.5 and $0.00295 on Kimi K2.6; repository review costs $0.34 and $0.0595; the cache-heavy agent loop costs $0.5 and $0.249. Kimi K2.6 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-5.5 or Kimi K2.6?
GPT-5.5 has the larger documented context window: 1M, compared with 256K.
Related comparisons
Last updated September 14, 2026
Watch GPT-5.5 vs Kimi K2.6
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.