Long documents
Prompts that approach the documented context limit
Kimi K2.6
Kimi K2.6 has the larger documented context window.
Confidence: documented
GPT-5 mini vs Kimi K2.6
Updated October 10, 2026. Rank says Kimi K2.6 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
Share or export
Decision reading
Kimi K2.6 has the higher public point estimate, 58.74 versus 48.75. Their conditional score ranges overlap. These ranges do not establish rank confidence. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 1
- GPT-5 mini only
- 0
- Kimi K2.6 only
- 36
- Like-for-like categories
- 0 / 8
Supported: GPT-5 mini · Estimated: Kimi K2.6. Conditional ranges do not establish rank confidence.How 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
GPT-5 mini
GPT-5 mini 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
GPT-5 mini
GPT-5 mini 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
GPT-5 mini is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-5 mini is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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. GPT-5 mini does not fit this workload in one request. GPT-5 mini 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.
27.8GPT-5 mini43.9Kimi K2.6
Directional only · BenchAlign v5.8
Kimi K2.6 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.
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.
1 Shared
36 Kimi K2.6 only
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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
Category results, on a stated basis
Each 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.
Agentic
Directional only- GPT-5 mini
- 27.1
- Estimated · #94/123
- Kimi K2.6
- 43.7
- Supported · #57/123
- Basis
- BenchAlign v5.8 lane · 0 vs 12 public rows
- Reading
- Directional only
Coding
Directional only- GPT-5 mini
- 27.8
- Estimated · #98/146
- Kimi K2.6
- 43.9
- Supported · #54/146
- Basis
- BenchAlign v5.8 lane · 1 vs 10 public rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5 mini
- Not ranked
- Kimi K2.6
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5 mini
- Not ranked
- Kimi K2.6
- 73.8
- #27/54
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5 mini
- Not ranked
- Kimi K2.6
- 59.8
- Supported · #45/177
- Basis
- BenchAlign v5.8 lane · 0 vs 5 public rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5 mini
- Not ranked
- Kimi K2.6
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5 mini
- Not ranked
- Kimi K2.6
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5 mini
- Not ranked
- Kimi K2.6
- 72.2
- #1/7
- Basis
- Provisional lane · 0 vs 4 weighted rows
- Reading
- Not comparable
| Category | GPT-5 mini | Kimi K2.6 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 27.1Estimated · #94/123 | 43.7Supported · #57/123 | Directional onlyBenchAlign v5.8 lane · 0 vs 12 public rows | Directional only |
| Coding | 27.8Estimated · #98/146 | 43.9Supported · #54/146 | Directional onlyBenchAlign v5.8 lane · 1 vs 10 public rows | Directional only |
| Reasoning | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 73.8#27/54 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Knowledge | Not ranked | 59.8Supported · #45/177 | Not comparableBenchAlign v5.8 lane · 0 vs 5 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 72.2#1/7 | Not comparableProvisional lane · 0 vs 4 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.
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 mini
- $0.00125
- Fits in one request
- Kimi K2.6
- $0.00295
- Fits in one request
GPT-5 mini has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-5 mini
- $0.0185
- Fits in one request
- Kimi K2.6
- $0.0595
- Fits in one request
GPT-5 mini 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 mini
- $0.075
- Does not fit in one request
- Cached input priced at the published list-input rate
- Kimi K2.6
- $0.091
- Fits in one request
GPT-5 mini does not fit this workload in one request. GPT-5 mini 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 miniKimi K2.6
Context window
Maximum documented context; output-token limits may be lower.
GPT-5 mini
128K
Kimi K2.6
256K
API model ID
GPT-5 mini
Not sourced
Kimi 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 mini
Not published
Kimi K2.6
$0.16 per 1M cached input tokens
Documented inputs
GPT-5 mini
Not sourced
Kimi K2.6
Not sourced
Documented outputs
GPT-5 mini
Not sourced
Kimi K2.6
Not sourced
Provider availability
GPT-5 mini
Not sourced
Kimi K2.6
Not sourced
Reasoning profile
GPT-5 mini
Reasoning
Kimi K2.6
Reasoning
Weight access
GPT-5 mini
Proprietary
Kimi K2.6
Open Weight
License
GPT-5 mini
Proprietary
Kimi K2.6
Open Weight
Release date
GPT-5 mini
2025-08-07
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
- Kimi K2.6 has the higher public point estimate, 58.74 versus 48.75. Their conditional score ranges overlap. These ranges do not establish rank confidence.
- Workload cost
- Repository review: $0.0185 vs $0.0595. Cache-heavy agent loop: $0.075 vs $0.091.
- Context tradeoff
- Kimi K2.6 has the larger documented window (256K).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-5 mini or Kimi K2.6?
Kimi K2.6 has the higher public point estimate, 58.74 versus 48.75. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-5 mini or Kimi K2.6?
Kimi K2.6 scores higher for coding on the public lane, 43.9 to 27.8. GPT-5 mini is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, GPT-5 mini or Kimi K2.6?
Kimi K2.6 scores higher for agentic tasks on the public lane, 43.7 to 27.1. GPT-5 mini is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, GPT-5 mini or Kimi K2.6?
For the stated presets, chat costs $0.00125 on GPT-5 mini and $0.00295 on Kimi K2.6; repository review costs $0.0185 and $0.0595; the cache-heavy agent loop costs $0.075 and $0.091. GPT-5 mini does not fit this workload in one request. GPT-5 mini has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-5 mini or Kimi K2.6?
Kimi K2.6 has the larger documented context window: 256K, compared with 128K.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
GPT-5 mini
API / mo$1,688
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 evidence37 rows
Agentic
Terminal-Bench 2.0
GPT-5 mini—Kimi K2.666.7%SourceNot directly comparable
BrowseComp
GPT-5 mini—Kimi K2.683.2%SourceNot directly comparable
OSWorld-Verified
GPT-5 mini—Kimi K2.673.1%SourceNot directly comparable
Toolathlon
GPT-5 mini—Kimi K2.650%SourceNot directly comparable
MCP Atlas
GPT-5 mini—Kimi K2.655.9%SourceNot directly comparable
Claw-Eval
GPT-5 mini—Kimi K2.662.3%SourceNot directly comparable
DeepSearchQA
GPT-5 mini—Kimi K2.692.5%SourceNot directly comparable
WideResearch
GPT-5 mini—Kimi K2.680.8%SourceNot directly comparable
Gert Labs
GPT-5 mini—Kimi K2.656.82%SourceNot directly comparable
ResearchClawBench
GPT-5 mini—Kimi K2.618.0%SourceNot directly comparable
OSWorld 2.0
GPT-5 mini—Kimi K2.64.6%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5 mini—Kimi K2.653.6%SourceNot directly comparable
Coding
Vibe Code Bench
Shared sourceGPT-5 mini14.17%Kimi K2.637.89%Kimi K2.6 leads this result
SWE-bench Verified
GPT-5 mini—Kimi K2.680.2%SourceNot directly comparable
LiveCodeBench v6
GPT-5 mini—Kimi K2.689.6%SourceNot directly comparable
SWE-bench Pro
GPT-5 mini—Kimi K2.658.6%SourceNot directly comparable
SWE Multilingual
GPT-5 mini—Kimi K2.676.7%SourceNot directly comparable
SciCode
GPT-5 mini—Kimi K2.652.2%SourceNot directly comparable
Terminal-Bench 2.0
GPT-5 mini—Kimi K2.666.7%SourceNot directly comparable
cursorBench31
GPT-5 mini—Kimi K2.647.6%SourceNot directly comparable
LiveCodeBench (Vals)
GPT-5 mini—Kimi K2.686.8%SourceNot directly comparable
SWE-bench (Vals)
GPT-5 mini—Kimi K2.676.2%SourceNot directly comparable
Multimodal
MMMU-Pro
GPT-5 mini—Kimi K2.679.4%SourceNot directly comparable
MMMU-Pro w/ Python
GPT-5 mini—Kimi K2.680.1%SourceNot directly comparable
CharXiv
GPT-5 mini—Kimi K2.680.4%SourceNot directly comparable
MathVision
GPT-5 mini—Kimi K2.687.4%SourceNot directly comparable
V*
GPT-5 mini—Kimi K2.696.9%SourceNot directly comparable
Knowledge
GPQA
GPT-5 mini—Kimi K2.690.5%SourceNot directly comparable
GPQA-D
GPT-5 mini—Kimi K2.690.5%SourceNot directly comparable
HLE
GPT-5 mini—Kimi K2.634.7%SourceNot directly comparable
GPQA Diamond (Vals)
GPT-5 mini—Kimi K2.689.1%SourceNot directly comparable
MMLU-Pro (Vals)
GPT-5 mini—Kimi K2.687.6%SourceNot directly comparable
Math
AIME26
GPT-5 mini—Kimi K2.696.4%SourceNot directly comparable
HMMT Feb 2026
GPT-5 mini—Kimi K2.692.7%SourceNot directly comparable
MMAnswerBench
GPT-5 mini—Kimi K2.686.0%SourceNot directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5 mini—Kimi K2.638.966%SourceNot directly comparable
FrontierMath v2 (Tier 4)
GPT-5 mini—Kimi K2.614.580%SourceNot directly comparable
37 public results · 1 shared
Watch GPT-5 mini vs Kimi K2.6
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 5,500+ readers.
Last updated October 10, 2026