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
GPT-5.6 Sol vs Kimi K2
Updated September 24, 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
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Decision reading
GPT-5.6 Sol has the higher public score, 78.49 versus 26.42, and the 90% score intervals do not overlap. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 2
- GPT-5.6 Sol only
- 35
- Kimi K2 only
- 0
- Like-for-like categories
- 0 / 8
Supported: GPT-5.6 Sol · Estimated: Kimi K2How the comparison works
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.
Chat turn cost
1K fresh input + 500 output tokens
Kimi K2
Kimi K2 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 K2
Kimi K2 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Kimi K2 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Kimi K2 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Kimi K2 does not fit this workload in one request. Kimi K2 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Which one for a specific job
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.
71.6GPT-5.6 Sol—Kimi K2
Not comparable · BenchAlign v5.7
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
35 GPT-5.6 Sol only
2 Shared
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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
Normalized gap 83.0GPT-5.6 Sol:83.000%Kimi K2:0.000%FrontierMath v2 (Tiers 1-3)Math
Normalized gap 67.6GPT-5.6 Sol:89.000%Kimi K2:21.404%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.7 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.
Knowledge
Directional only- GPT-5.6 Sol
- 78.8
- Supported · #7/158
- Kimi K2
- 37.2
- Estimated · #98/158
- Basis
- BenchAlign v5.7 lane · 8 vs 0 public rows
- Reading
- Directional only
Instruction following
Directional only- GPT-5.6 Sol
- 87.7
- #28/124
- Kimi K2
- 47.0
- #85/124
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Agentic
Not comparable- GPT-5.6 Sol
- 69.6
- Supported · #7/105
- Kimi K2
- Not ranked
- Basis
- BenchAlign v5.7 lane · 9 vs 0 public rows
- Reading
- Not comparable
Coding
Not comparable- GPT-5.6 Sol
- 71.6
- Supported · #6/135
- Kimi K2
- Not ranked
- Basis
- BenchAlign v5.7 lane · 12 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.6 Sol
- 72.1
- #8/19
- Kimi K2
- 58.5
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.6 Sol
- 87.6
- #5/50
- Kimi K2
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.6 Sol
- Not ranked
- Kimi K2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.6 Sol
- 96.8
- Unranked · 3 rankable rows
- Kimi K2
- 39.0
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Not comparable
| Category | GPT-5.6 Sol | Kimi K2 | Basis | Reading |
|---|---|---|---|---|
| Knowledge | 78.8Supported · #7/158 | 37.2Estimated · #98/158 | Directional onlyBenchAlign v5.7 lane · 8 vs 0 public rows | Directional only |
| Instruction following | 87.7#28/124 | 47.0#85/124 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Agentic | 69.6Supported · #7/105 | Not ranked | Not comparableBenchAlign v5.7 lane · 9 vs 0 public rows | Not comparable |
| Coding | 71.6Supported · #6/135 | Not ranked | Not comparableBenchAlign v5.7 lane · 12 vs 0 public rows | Not comparable |
| Reasoning | 72.1#8/19 | 58.5Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multimodal | 87.6#5/50 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 96.8Unranked · 3 rankable rows | 39.0Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 2 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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.
Supported evidence per lane · bars run 0–100Methodology
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
- $0.014
- Fits in one request
- Kimi K2
- $0.00185
- Fits in one request
Kimi K2 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-5.6 Sol
- $0.26
- Fits in one request
- Kimi K2
- $0.0375
- Fits in one request
Kimi K2 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
- $0.36
- Fits in one request
- Kimi K2
- $0.157
- Does not fit in one request
- Cached input priced at the published list-input rate
Kimi K2 does not fit this workload in one request. Kimi K2 has no published cached-input rate, so cached tokens use its listed input rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
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
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.6 Sol
1.05M
OpenAI model catalogKimi K2
128K
API model ID
GPT-5.6 Sol
gpt-5.6-sol
OpenAI model catalogKimi K2
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
$0.4 per 1M cached input tokens
OpenAI pricingKimi K2
Not published
Documented inputs
GPT-5.6 Sol
text, image
OpenAI model catalogKimi K2
Not sourced
Documented outputs
GPT-5.6 Sol
Kimi K2
Not sourced
Provider availability
GPT-5.6 Sol
Generally Available · OpenAI Responses API
OpenAI model catalogKimi K2
Not sourced
Reasoning profile
GPT-5.6 Sol
Reasoning
Kimi K2
Non-Reasoning
Weight access
GPT-5.6 Sol
Proprietary
Kimi K2
Proprietary
License
GPT-5.6 Sol
Proprietary
Kimi K2
Proprietary
Release date
GPT-5.6 Sol
2026-07-09
Kimi K2
2025-07-01
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, 78.49 versus 26.42, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.26 vs $0.0375. Cache-heavy agent loop: $0.36 vs $0.157.
- 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.
Questions
Which is better, GPT-5.6 Sol or Kimi K2?
GPT-5.6 Sol has the higher public score, 78.49 versus 26.42, 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?
Kimi K2 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GPT-5.6 Sol or Kimi K2?
Kimi K2 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-5.6 Sol or Kimi K2?
For the stated presets, chat costs $0.014 on GPT-5.6 Sol and $0.00185 on Kimi K2; repository review costs $0.26 and $0.0375; the cache-heavy agent loop costs $0.36 and $0.157. Kimi K2 does not fit this workload in one request. Kimi K2 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?
GPT-5.6 Sol has the larger documented context window: 1.05M, compared with 128K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence37 rows
Agentic
Terminal-Bench 3.0
GPT-5.6 Sol34.6%SourceKimi K2—Not directly comparable
Terminal-Bench 2.1
GPT-5.6 Sol91.9%SourceKimi K2—Not directly comparable
BrowseComp
GPT-5.6 Sol92.2%SourceKimi K2—Not directly comparable
OSWorld 2.0
GPT-5.6 Sol62.6%SourceKimi K2—Not directly comparable
CyberGym
GPT-5.6 Sol84.5%SourceKimi K2—Not directly comparable
ExploitGym
GPT-5.6 Sol33.7%SourceKimi K2—Not directly comparable
Toolathlon
GPT-5.6 Sol58%SourceKimi K2—Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.6 Sol85.8%SourceKimi K2—Not directly comparable
ApprenticeBench
GPT-5.6 Sol26%SourceKimi K2—Not directly comparable
Coding
Bug Hunt Bench
GPT-5.6 Sol42 fixesSourceKimi K2—Not directly comparable
SWE-bench Pro
GPT-5.6 Sol64.6%SourceKimi K2—Not directly comparable
Terminal-Bench 2.1
GPT-5.6 Sol91.9%SourceKimi K2—Not directly comparable
DeepSWE
GPT-5.6 Sol72.7%SourceKimi K2—Not directly comparable
FrontierCode 1.1 Extended
GPT-5.6 Sol60.6%SourceKimi K2—Not directly comparable
FrontierSWE v2
GPT-5.6 Sol32.2%SourceKimi K2—Not directly comparable
cursorBench32
GPT-5.6 Sol67.2%SourceKimi K2—Not directly comparable
VulcanBench v3
GPT-5.6 Sol87.0%SourceKimi K2—Not directly comparable
VulcanBench CII v1
GPT-5.6 Sol86.5%SourceKimi K2—Not directly comparable
LiveCodeBench (Vals)
GPT-5.6 Sol82.6%SourceKimi K2—Not directly comparable
SWE-bench (Vals)
GPT-5.6 Sol96.2%SourceKimi K2—Not directly comparable
cursorBench40
GPT-5.6 Sol41.7%SourceKimi K2—Not directly comparable
Reasoning
Multimodal
Knowledge
GPQA
GPT-5.6 Sol94.6%SourceKimi K2—Not directly comparable
GPQA-D
GPT-5.6 Sol94.6%SourceKimi K2—Not directly comparable
HLE-Verified
GPT-5.6 Sol54.5%SourceKimi K2—Not directly comparable
LABBench2
GPT-5.6 Sol82.1%SourceKimi K2—Not directly comparable
HealthBench Professional
GPT-5.6 Sol60.5%SourceKimi K2—Not directly comparable
HealthBench Hard
GPT-5.6 Sol33.1%SourceKimi K2—Not directly comparable
GPQA Diamond (Vals)
GPT-5.6 Sol95.2%SourceKimi K2—Not directly comparable
MMLU-Pro (Vals)
GPT-5.6 Sol89.1%SourceKimi K2—Not directly comparable
Math
37 public results · 2 shared
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Last updated September 24, 2026