Agentic
Directional only- GPT-5.6 Sol
- 92.0
- Kimi K2.6
- 73.5
- Weighted basis
- 2 vs 3 rows
- Reading
- Directional only
Model comparison
GPT-5.6 Sol vs Kimi K2.6
Updated August 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
GPT-5.6 Sol has the higher public score, 81.48 versus 59.02, and the 90% score intervals do not overlap.
12 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.
Long documents
Prompts that approach the documented context limit
GPT-5.6 Sol
GPT-5.6 Sol has the larger documented context window.
Confidence: documented
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
Show secondary and unsupported calls
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
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
12 GPT-5.6 Sol only
12 Shared
20 Kimi K2.6 only
- Shared results
- 12
- GPT-5.6 Sol only
- 12
- Kimi K2.6 only
- 20
- Like-for-like categories
- 0 / 8
5 categories use 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.
Coding
Directional only- GPT-5.6 Sol
- 64.6
- Kimi K2.6
- 64.4
- Weighted basis
- 1 vs 3 rows
- Reading
- Directional only
Knowledge
Directional only- GPT-5.6 Sol
- 94.6
- Kimi K2.6
- 42.2
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Math
Directional only- GPT-5.6 Sol
- 87.5
- Kimi K2.6
- 67.1
- Weighted basis
- 2 vs 4 rows
- Reading
- Directional only
Multimodal
Directional only- GPT-5.6 Sol
- 83.0
- Kimi K2.6
- 79.8
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.6 Sol
- 92.5
- Kimi K2.6
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.6 Sol
- Not measured
- Kimi K2.6
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.6 Sol
- Not measured
- Kimi K2.6
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GPT-5.6 Sol | Kimi K2.6 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 92.0 | 73.5 | Directional only2 vs 3 rows | Directional only |
| Coding | 64.6 | 64.4 | Directional only1 vs 3 rows | Directional only |
| Knowledge | 94.6 | 42.2 | Directional only1 vs 2 rows | Directional only |
| Math | 87.5 | 67.1 | Directional only2 vs 4 rows | Directional only |
| Multimodal | 83.0 | 79.8 | Directional only1 vs 2 rows | Directional only |
| Reasoning | 92.5 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 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
FrontierMath v2 (Tiers 1-3)
Math
Terminal-Bench 2.0
Agentic
BrowseComp
Agentic
SWE-bench Pro
Coding
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
- 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-5.6 Sol
- $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-5.6 Sol
- $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-5.6 SolKimi K2.6
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.6 Sol
1.05M
OpenAI model catalogKimi K2.6
256K
API model ID
GPT-5.6 Sol
gpt-5.6-sol
OpenAI model catalogKimi 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-5.6 Sol
$0.5 per 1M cached input tokens
OpenAI pricingKimi K2.6
Not published
Documented inputs
GPT-5.6 Sol
text, image
OpenAI model catalogKimi K2.6
Not sourced
Documented outputs
GPT-5.6 Sol
Kimi K2.6
Not sourced
Provider availability
GPT-5.6 Sol
Generally Available · OpenAI Responses API
OpenAI model catalogKimi K2.6
Not sourced
Reasoning profile
GPT-5.6 Sol
Reasoning
Kimi K2.6
Reasoning
Weight access
GPT-5.6 Sol
Proprietary
Kimi K2.6
Open Weight
License
GPT-5.6 Sol
Proprietary
Kimi K2.6
Open Weight
Release date
GPT-5.6 Sol
2026-07-09
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-5.6 Sol has the higher public score, 81.48 versus 59.02, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.34 vs $0.0595. Cache-heavy agent loop: $0.5 vs $0.249.
- Context tradeoff
- GPT-5.6 Sol has the larger documented window (1.05M).
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-5.6 Sol
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 evidence44 rows
Agentic
Terminal-Bench 2.0
GPT-5.6 Sol91.9%SourceKimi K2.666.7%SourceGPT-5.6 Sol leads this result
BrowseComp
GPT-5.6 Sol92.2%SourceKimi K2.683.2%SourceGPT-5.6 Sol leads this result
OSWorld 2.0
GPT-5.6 Sol62.6%SourceKimi K2.64.6%SourceGPT-5.6 Sol leads this result
CyberGym
GPT-5.6 Sol84.5%SourceKimi K2.6—Not directly comparable
ExploitGym
GPT-5.6 Sol33.7%SourceKimi K2.6—Not directly comparable
Toolathlon
GPT-5.6 Sol58%SourceKimi K2.650%SourceGPT-5.6 Sol leads this result
OSWorld-Verified
GPT-5.6 Sol—Kimi K2.673.1%SourceNot directly comparable
MCP Atlas
GPT-5.6 Sol—Kimi K2.655.9%SourceNot directly comparable
Claw-Eval
GPT-5.6 Sol—Kimi K2.662.3%SourceNot directly comparable
DeepSearchQA
GPT-5.6 Sol—Kimi K2.692.5%SourceNot directly comparable
WideResearch
GPT-5.6 Sol—Kimi K2.680.8%SourceNot directly comparable
Gert Labs
GPT-5.6 Sol—Kimi K2.656.82%SourceNot directly comparable
ResearchClawBench
GPT-5.6 Sol—Kimi K2.618.0%SourceNot directly comparable
Coding
SWE-bench Pro
GPT-5.6 Sol64.6%SourceKimi K2.658.6%SourceGPT-5.6 Sol leads this result
Terminal-Bench 2.0
GPT-5.6 Sol91.9%SourceKimi K2.666.7%SourceGPT-5.6 Sol leads this result
deepSwe
GPT-5.6 Sol72.7%SourceKimi K2.6—Not directly comparable
FrontierCode 1.1 Extended
GPT-5.6 Sol60.6%SourceKimi K2.6—Not directly comparable
cursorBench32
GPT-5.6 Sol67.2%SourceKimi K2.6—Not directly comparable
VulcanBench v3
GPT-5.6 Sol87.0%SourceKimi K2.6—Not directly comparable
SWE-bench Verified
GPT-5.6 Sol—Kimi K2.680.2%SourceNot directly comparable
LiveCodeBench v6
GPT-5.6 Sol—Kimi K2.689.6%SourceNot directly comparable
SWE Multilingual
GPT-5.6 Sol—Kimi K2.676.7%SourceNot directly comparable
SciCode
GPT-5.6 Sol—Kimi K2.652.2%SourceNot directly comparable
Vibe Code Bench
GPT-5.6 Sol—Kimi K2.637.89%SourceNot directly comparable
cursorBench31
GPT-5.6 Sol—Kimi K2.647.6%SourceNot directly comparable
Reasoning
Knowledge
GPQA
GPT-5.6 Sol94.6%SourceKimi K2.690.5%SourceGPT-5.6 Sol leads this result
GPQA-D
GPT-5.6 Sol94.6%SourceKimi K2.690.5%SourceGPT-5.6 Sol leads this result
HealthBench Professional
GPT-5.6 Sol60.5%SourceKimi K2.6—Not directly comparable
HealthBench Hard
GPT-5.6 Sol33.1%SourceKimi K2.6—Not directly comparable
HLE
GPT-5.6 Sol—Kimi K2.634.7%SourceNot directly comparable
Math
FrontierMath (legacy)
GPT-5.6 Sol89%SourceKimi K2.6—Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.6 Sol89.000%SourceKimi K2.638.966%SourceGPT-5.6 Sol leads this result
FrontierMath v2 (Tier 4)
GPT-5.6 Sol83.000%SourceKimi K2.614.580%SourceGPT-5.6 Sol leads this result
AIME26
GPT-5.6 Sol—Kimi K2.696.4%SourceNot directly comparable
HMMT Feb 2026
GPT-5.6 Sol—Kimi K2.692.7%SourceNot directly comparable
MMAnswerBench
GPT-5.6 Sol—Kimi K2.686.0%SourceNot directly comparable
Multimodal
MMMU-Pro
GPT-5.6 Sol83%SourceKimi K2.679.4%SourceGPT-5.6 Sol leads this result
MMMU-Pro w/ Python
GPT-5.6 Sol84.6%SourceKimi K2.680.1%SourceGPT-5.6 Sol leads this result
CharXiv
GPT-5.6 Sol—Kimi K2.680.4%SourceNot directly comparable
MathVision
GPT-5.6 Sol—Kimi K2.687.4%SourceNot directly comparable
V*
GPT-5.6 Sol—Kimi K2.696.9%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-5.6 Sol or Kimi K2.6?
GPT-5.6 Sol has the higher public score, 81.48 versus 59.02, 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-5.6 Sol or Kimi K2.6?
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, GPT-5.6 Sol or Kimi K2.6?
The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which costs less, GPT-5.6 Sol or Kimi K2.6?
For the stated presets, chat costs $0.02 on GPT-5.6 Sol 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-5.6 Sol or Kimi K2.6?
GPT-5.6 Sol has the larger documented context window: 1.05M, compared with 256K.
Related comparisons
Last updated August 10, 2026
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