Updated October 2, 2026. Rank says GPT-5.6 Sol is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
GPT-5.6 Sol has the higher public score, 78.95 versus 72.14, and the 90% score intervals do not overlap. 19 results are shared. Category rows resting on Estimated evidence or 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.
Code generation, repair, and software-engineering tasks
GPT-5.6 Sol
GPT-5.6 Sol has the higher public coding point estimate, 70.5 to 61.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
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.
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.
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.
Tool use, computer use, and multi-step task completion
No clear pick
The like-for-like agentic result is a practical tie on the public lane (within 0.5 points).
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
Like-for-like · BenchAlign v5.8
GPT-5.6 Sol has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
ARC-AGI-2Reasoning
Normalized gap 32.1CursorBench 3.2Coding
Normalized gap 6.4LiveCodeBench (Vals)Coding
Normalized gap 4.6MMMU-ProMultimodal
Normalized gap 1.4GPQAKnowledge
Normalized gap 1.1Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.
| Category | GPT-5.6 Sol | Kimi K3 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 68.3Supported · #7/119 | 68.1Supported · #8/119 | Like-for-likeBenchAlign v5.8 lane · 9 vs 12 public rows | Practical tie |
| Coding | 70.5Supported · #8/144 | 61.4Supported · #18/144 | Like-for-likeBenchAlign v5.8 lane · 13 vs 14 public rows | GPT-5.6 Sol leads · intervals overlap |
| Knowledge | 78.1Supported · #9/171 | 67.9Supported · #18/171 | Like-for-likeBenchAlign v5.8 lane · 8 vs 6 public rows | GPT-5.6 Sol leads · intervals overlap |
| Reasoning | 72.5#15/27 | 65.8#18/27 | Directional onlyProvisional lane · 2 vs 1 weighted rows | Directional only |
| Multimodal | 88.6#4/49 | 89.4#1/49 | Directional onlyProvisional lane · 1 vs 3 weighted rows | Directional only |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 87.7#27/125 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 96.8Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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 K3 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Kimi K3 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Kimi K3 has the lower modeled cost
Costs use the listed standard API rates.
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-5.6 Sol
1.05M
OpenAI model catalogKimi K3
1.05M
GPT-5.6 Sol
gpt-5.6-sol
OpenAI model catalogKimi K3
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.6 Sol
$0.4 per 1M cached input tokens
OpenAI pricingKimi K3
$0.3 per 1M cached input tokens
GPT-5.6 Sol
text, image
OpenAI model catalogKimi K3
Not sourced
GPT-5.6 Sol
Kimi K3
Not sourced
GPT-5.6 Sol
Generally Available · OpenAI Responses API
OpenAI model catalogKimi K3
Not sourced
GPT-5.6 Sol
Reasoning
Kimi K3
Reasoning
GPT-5.6 Sol
Proprietary
Kimi K3
Pending
GPT-5.6 Sol
Proprietary
Kimi K3
Pending
GPT-5.6 Sol
2026-07-09
Kimi K3
2026-07-16
GPT-5.6 Sol has the higher public score, 78.95 versus 72.14, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
GPT-5.6 Sol has the higher public coding point estimate, 70.5 to 61.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
The like-for-like agentic tasks row is a practical tie on the public lane, 68.3 against 68.1, inside the 0.5-point band BenchLM treats as level.
For the stated presets, chat costs $0.014 on GPT-5.6 Sol and $0.0105 on Kimi K3; repository review costs $0.26 and $0.195; the cache-heavy agent loop costs $0.36 and $0.27. Costs use the listed standard API rates.
Both models list the same context window, 1.05M.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 3.0
Not directly comparable
Terminal-Bench 2.1
GPT-5.6 Sol leads this result
BrowseComp
GPT-5.6 Sol leads this result
OSWorld 2.0
Not directly comparable
CyberGym
Not directly comparable
ExploitGym
Not directly comparable
Toolathlon
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.6 Sol leads this result
ApprenticeBench
Shared sourceGPT-5.6 Sol leads this result
DeepSearchQA
Not directly comparable
Toolathlon-Verified
Not directly comparable
MCP Atlas
Not directly comparable
AutomationBench
Not directly comparable
JobBench
Not directly comparable
APEX-Agents
Not directly comparable
SpreadsheetBench 2
Not directly comparable
DECK-Bench
Not directly comparable
Bug Hunt Bench
Not directly comparable
SWE-bench Pro
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
DeepSWE
GPT-5.6 Sol leads this result
FrontierCode 1.1 Extended
Not directly comparable
FrontierSWE v2
Shared sourceGPT-5.6 Sol leads this result
CursorBench 3.2
Shared sourceGPT-5.6 Sol leads this result
VulcanBench v3
GPT-5.6 Sol leads this result
VulcanBench CII v1
Not directly comparable
LiveCodeBench (Vals)
Kimi K3 leads this result
SWE-bench (Vals)
GPT-5.6 Sol leads this result
CursorBench 4.0
Not directly comparable
PostTrainBench v1.1
Shared sourceGPT-5.6 Sol leads this result
FrontierSWE
Not directly comparable
ProgramBench
Not directly comparable
Kimi Code Bench v2
Not directly comparable
sweMarathon
Not directly comparable
PostTrain Bench
Not directly comparable
MLS-Bench Lite
Not directly comparable
OpenHarmony Bench
Not directly comparable
MMMU-Pro
GPT-5.6 Sol leads this result
MMMU-Pro w/ Python
GPT-5.6 Sol leads this result
OfficeQA Pro
Not directly comparable
CharXiv w/o tools
Not directly comparable
CharXiv
Not directly comparable
MathVision
Not directly comparable
MathVision w/ Python
Not directly comparable
BabyVision w/ Python
Not directly comparable
ZeroBench
Not directly comparable
ZeroBench w/ Python
Not directly comparable
WorldVQA ForceAnswer
Not directly comparable
OmniDocBench
Not directly comparable
PerceptionBench
Not directly comparable
GPQA
GPT-5.6 Sol leads this result
GPQA-D
GPT-5.6 Sol leads this result
HLE-Verified
Not directly comparable
LABBench2
Not directly comparable
HealthBench Professional
Not directly comparable
HealthBench Hard
Not directly comparable
GPQA Diamond (Vals)
GPT-5.6 Sol leads this result
MMLU-Pro (Vals)
GPT-5.6 Sol leads this result
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
Gray Swan IPI (15 attempts)
Shared sourceGPT-5.6 Sol leads this result
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Last updated October 2, 2026
