Agentic
Like-for-like- GPT-5.6 Sol
- 92.0
- Kimi K3
- 89.5
- Weighted basis
- 2 vs 2 rows
- Reading
- GPT-5.6 Sol leads
GPT-5.6 Sol vs Kimi K3
Updated August 19, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GPT-5.6 Sol has the higher public score estimate, 82.04 versus 80.53, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
13 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.
Agentic work
Tool use, computer use, and multi-step task completion
GPT-5.6 Sol
GPT-5.6 Sol leads on the same 2 weighted benchmark rows.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Kimi K3
Kimi K3 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 K3
Kimi K3 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
Kimi K3
Kimi K3 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
No shared weighted benchmark basis supports a winner.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
19 GPT-5.6 Sol only
13 Shared
27 Kimi K3 only
- Shared results
- 13
- GPT-5.6 Sol only
- 19
- Kimi K3 only
- 27
- Like-for-like categories
- 1 / 8
2 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.
Knowledge
Directional only- GPT-5.6 Sol
- 94.6
- Kimi K3
- 61.0
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Multimodal
Directional only- GPT-5.6 Sol
- 83.0
- Kimi K3
- 78.5
- Weighted basis
- 1 vs 3 rows
- Reading
- Directional only
Coding
Not comparable- GPT-5.6 Sol
- 64.6
- Kimi K3
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.6 Sol
- 92.5
- Kimi K3
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.6 Sol
- 87.5
- Kimi K3
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.6 Sol
- Not measured
- Kimi K3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.6 Sol
- Not measured
- Kimi K3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GPT-5.6 Sol | Kimi K3 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 92.0 | 89.5 | Like-for-like2 vs 2 rows | GPT-5.6 Sol leads |
| Knowledge | 94.6 | 61.0 | Directional only1 vs 2 rows | Directional only |
| Multimodal | 83.0 | 78.5 | Directional only1 vs 3 rows | Directional only |
| Coding | 64.6 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Reasoning | 92.5 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Math | 87.5 | Not measured | Not comparable2 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.
Terminal-Bench 2.0
Agentic
MMMU-Pro
Multimodal
GPQA
Knowledge
BrowseComp
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-5.6 Sol
- $0.02
- Fits in one request
- Kimi K3
- $0.0105
- Fits in one request
Kimi K3 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 K3
- $0.195
- Fits in one request
Kimi K3 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 K3
- $0.27
- Fits in one request
Kimi K3 has the lower modeled cost
Costs use the listed standard API rates.
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 K3
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.6 Sol
1.05M
OpenAI model catalogKimi K3
1.05M
API model ID
GPT-5.6 Sol
gpt-5.6-sol
OpenAI model catalogKimi K3
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 K3
$0.3 per 1M cached input tokens
Documented inputs
GPT-5.6 Sol
text, image
OpenAI model catalogKimi K3
Not sourced
Documented outputs
GPT-5.6 Sol
Kimi K3
Not sourced
Provider availability
GPT-5.6 Sol
Generally Available · OpenAI Responses API
OpenAI model catalogKimi K3
Not sourced
Reasoning profile
GPT-5.6 Sol
Reasoning
Kimi K3
Reasoning
Weight access
GPT-5.6 Sol
Proprietary
Kimi K3
Pending
License
GPT-5.6 Sol
Proprietary
Kimi K3
Pending
Release date
GPT-5.6 Sol
2026-07-09
Kimi K3
2026-07-16
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 estimate, 82.04 versus 80.53, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.34 vs $0.195. Cache-heavy agent loop: $0.5 vs $0.27.
- Context tradeoff
- Both models list 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 evidence59 rows
Agentic
Terminal-Bench 3.0
GPT-5.6 Sol34.6%SourceKimi K3—Not directly comparable
Terminal-Bench 2.0
GPT-5.6 Sol91.9%SourceKimi K388.3%SourceGPT-5.6 Sol leads this result
BrowseComp
GPT-5.6 Sol92.2%SourceKimi K391.2%SourceGPT-5.6 Sol leads this result
OSWorld 2.0
GPT-5.6 Sol62.6%SourceKimi K3—Not directly comparable
CyberGym
GPT-5.6 Sol84.5%SourceKimi K3—Not directly comparable
ExploitGym
GPT-5.6 Sol33.7%SourceKimi K3—Not directly comparable
Toolathlon
GPT-5.6 Sol58%SourceKimi K3—Not directly comparable
DeepSearchQA
GPT-5.6 Sol—Kimi K395.0%SourceNot directly comparable
Toolathlon-Verified
GPT-5.6 Sol—Kimi K373.2%SourceNot directly comparable
MCP Atlas
GPT-5.6 Sol—Kimi K384.2%SourceNot directly comparable
AutomationBench
GPT-5.6 Sol—Kimi K330.8%SourceNot directly comparable
JobBench
GPT-5.6 Sol—Kimi K352.9%SourceNot directly comparable
APEX-Agents
GPT-5.6 Sol—Kimi K337.6%SourceNot directly comparable
SpreadsheetBench 2
GPT-5.6 Sol—Kimi K334.8%SourceNot directly comparable
DECK-Bench
GPT-5.6 Sol—Kimi K373.5%SourceNot directly comparable
Coding
SWE-bench Pro
GPT-5.6 Sol64.6%SourceKimi K3—Not directly comparable
Terminal-Bench 2.0
GPT-5.6 Sol91.9%SourceKimi K3—Not directly comparable
deepSwe
GPT-5.6 Sol72.7%SourceKimi K367.5%SourceGPT-5.6 Sol leads this result
FrontierCode 1.1 Extended
GPT-5.6 Sol60.6%SourceKimi K3—Not directly comparable
cursorBench32
Shared sourceGPT-5.6 Sol67.2%Kimi K360.8%GPT-5.6 Sol leads this result
VulcanBench v3
GPT-5.6 Sol87.0%SourceKimi K373.9%SourceGPT-5.6 Sol leads this result
APEX-SWE
Shared sourceGPT-5.6 Sol45.8%Kimi K348.0%Kimi K3 leads this result
EEBench
Shared sourceGPT-5.6 Sol39.4%Kimi K338.3%GPT-5.6 Sol leads this result
CADGenBench Generation
GPT-5.6 Sol37.1%SourceKimi K3—Not directly comparable
CADBench
GPT-5.6 Sol86.5%SourceKimi K3—Not directly comparable
SpaceXAI MTS Eval
GPT-5.6 Sol52.1%SourceKimi K3—Not directly comparable
InferenceEval
Shared sourceGPT-5.6 Sol44.1%Kimi K339.8%GPT-5.6 Sol leads this result
KernelBench Internal
Shared sourceGPT-5.6 Sol73.1%Kimi K368.5%GPT-5.6 Sol leads this result
FrontierSWE
GPT-5.6 Sol—Kimi K381.2%SourceNot directly comparable
ProgramBench
GPT-5.6 Sol—Kimi K377.8%SourceNot directly comparable
Kimi Code Bench v2
GPT-5.6 Sol—Kimi K372.9%SourceNot directly comparable
sweMarathon
GPT-5.6 Sol—Kimi K342%SourceNot directly comparable
PostTrain Bench
GPT-5.6 Sol—Kimi K336.6%SourceNot directly comparable
MLS-Bench Lite
GPT-5.6 Sol—Kimi K348.3%SourceNot directly comparable
Reasoning
Knowledge
GPQA
GPT-5.6 Sol94.6%SourceKimi K393.5%SourceGPT-5.6 Sol leads this result
GPQA-D
GPT-5.6 Sol94.6%SourceKimi K393.5%SourceGPT-5.6 Sol leads this result
HealthBench Professional
GPT-5.6 Sol60.5%SourceKimi K3—Not directly comparable
HealthBench Hard
GPT-5.6 Sol33.1%SourceKimi K3—Not directly comparable
HLE
GPT-5.6 Sol—Kimi K356%SourceNot directly comparable
HLE w/o tools
GPT-5.6 Sol—Kimi K343.5%SourceNot directly comparable
Math
Multimodal
MMMU-Pro
GPT-5.6 Sol83%SourceKimi K381.6%SourceGPT-5.6 Sol leads this result
MMMU-Pro w/ Python
GPT-5.6 Sol84.6%SourceKimi K383.4%SourceGPT-5.6 Sol leads this result
OfficeQA Pro
GPT-5.6 Sol—Kimi K363.3%SourceNot directly comparable
CharXiv w/o tools
GPT-5.6 Sol—Kimi K384.8%SourceNot directly comparable
CharXiv
GPT-5.6 Sol—Kimi K391.3%SourceNot directly comparable
MathVision
GPT-5.6 Sol—Kimi K394.3%SourceNot directly comparable
MathVision w/ Python
GPT-5.6 Sol—Kimi K397.8%SourceNot directly comparable
BabyVision w/ Python
GPT-5.6 Sol—Kimi K385.7%SourceNot directly comparable
ZeroBench
GPT-5.6 Sol—Kimi K323.0%SourceNot directly comparable
ZeroBench w/ Python
GPT-5.6 Sol—Kimi K341.0%SourceNot directly comparable
WorldVQA ForceAnswer
GPT-5.6 Sol—Kimi K351.0%SourceNot directly comparable
OmniDocBench
GPT-5.6 Sol—Kimi K391.1%SourceNot directly comparable
PerceptionBench
GPT-5.6 Sol—Kimi K358.5%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-5.6 Sol or Kimi K3?
GPT-5.6 Sol has the higher public score estimate, 82.04 versus 80.53, 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-5.6 Sol or Kimi K3?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, GPT-5.6 Sol or Kimi K3?
GPT-5.6 Sol leads the like-for-like agentic tasks comparison across 2 shared weighted benchmark rows.
Which costs less, GPT-5.6 Sol or Kimi K3?
For the stated presets, chat costs $0.02 on GPT-5.6 Sol and $0.0105 on Kimi K3; repository review costs $0.34 and $0.195; the cache-heavy agent loop costs $0.5 and $0.27. Costs use the listed standard API rates.
Which has the larger context window, GPT-5.6 Sol or Kimi K3?
Both models list the same context window, 1.05M.
Related comparisons
Last updated August 19, 2026
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