Coding work
Code generation, repair, and software-engineering tasks
Gemini 4 Argon
Gemini 4 Argon leads on the public coding lane, 68.4 to 46.4, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Gemini 4 Argon vs Kimi K2.6
Updated September 30, 2026. Rank says Gemini 4 Argon 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
Gemini 4 Argon has the higher public score estimate, 64.59 versus 59.22, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 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
- Gemini 4 Argon only
- 15
- Kimi K2.6 only
- 35
- Like-for-like categories
- 2 / 8
Estimated: Gemini 4 Argon and Kimi K2.6How 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 K2.6
Kimi K2.6 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCache-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. 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 K2.6
Kimi K2.6 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemini 4 Argon is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedLong documents
Prompts that approach the documented context limit
Not enough matched evidence
A complete context comparison is not sourced.
Confidence: limited
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.
68.4Gemini 4 Argon46.4Kimi K2.6
Like-for-like · BenchAlign v5.7
Gemini 4 Argon leads the like-for-like coding row.
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.
15 Gemini 4 Argon only
2 Shared
35 Kimi K2.6 only
1 category rests 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.
OSWorld 2.0Agentic
Normalized gap 64.6Gemini 4 Argon:69.2%Kimi K2.6:4.6%
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.
Coding
Like-for-like- Gemini 4 Argon
- 68.4
- Supported · #8/144
- Kimi K2.6
- 46.4
- Supported · #50/144
- Basis
- BenchAlign v5.7 lane · 4 vs 10 public rows
- Reading
- Gemini 4 Argon leads
Knowledge
Like-for-like- Gemini 4 Argon
- 72.9
- Supported · #11/170
- Kimi K2.6
- 59.1
- Supported · #42/170
- Basis
- BenchAlign v5.7 lane · 1 vs 5 public rows
- Reading
- Gemini 4 Argon leads · intervals overlap
Agentic
Directional only- Gemini 4 Argon
- 63.7
- Estimated · #14/119
- Kimi K2.6
- 44.0
- Supported · #52/119
- Basis
- BenchAlign v5.7 lane · 7 vs 12 public rows
- Reading
- Directional only
Reasoning
Not comparable- Gemini 4 Argon
- 77.1
- Unranked · 3 rankable rows
- Kimi K2.6
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 4 Argon
- Not ranked
- Kimi K2.6
- 64.9
- #26/49
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 4 Argon
- Not ranked
- Kimi K2.6
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 4 Argon
- Not ranked
- Kimi K2.6
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 4 Argon
- Not ranked
- Kimi K2.6
- 71.0
- #1/7
- Basis
- Provisional lane · 0 vs 4 weighted rows
- Reading
- Not comparable
| Category | Gemini 4 Argon | Kimi K2.6 | Basis | Reading |
|---|---|---|---|---|
| Coding | 68.4Supported · #8/144 | 46.4Supported · #50/144 | Like-for-likeBenchAlign v5.7 lane · 4 vs 10 public rows | Gemini 4 Argon leads |
| Knowledge | 72.9Supported · #11/170 | 59.1Supported · #42/170 | Like-for-likeBenchAlign v5.7 lane · 1 vs 5 public rows | Gemini 4 Argon leads · intervals overlap |
| Agentic | 63.7Estimated · #14/119 | 44.0Supported · #52/119 | Directional onlyBenchAlign v5.7 lane · 7 vs 12 public rows | Directional only |
| Reasoning | 77.1Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 64.9#26/49 | Not comparableProvisional lane · 0 vs 2 weighted 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 | 71.0#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 v5.7) 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
- Gemini 4 Argon
- $0.007
- Fit state unavailable
- 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
- Gemini 4 Argon
- $0.13
- Fit state unavailable
- 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
- Gemini 4 Argon
- $0.16
- Fit state unavailable
- Kimi K2.6
- $0.091
- Fits in one request
Kimi K2.6 has the lower modeled cost
Costs use the listed standard API rates.
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.
SpecificationGemini 4 ArgonKimi K2.6
Context window
Maximum documented context; output-token limits may be lower.
Gemini 4 Argon
Not sourced
Google Gemini 4 Argon announcementKimi K2.6
256K
API model ID
Gemini 4 Argon
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.
Gemini 4 Argon
$0.1 per 1M cached input tokens
Google Gemini 4 Argon announcementKimi K2.6
$0.16 per 1M cached input tokens
Documented inputs
Gemini 4 Argon
Not sourced
Kimi K2.6
Not sourced
Documented outputs
Gemini 4 Argon
Not sourced
Kimi K2.6
Not sourced
Provider availability
Gemini 4 Argon
Limited Access · Fairwind Program
Google Gemini 4 Argon announcementKimi K2.6
Not sourced
Reasoning profile
Gemini 4 Argon
Reasoning
Kimi K2.6
Reasoning
Weight access
Gemini 4 Argon
Proprietary
Kimi K2.6
Open Weight
License
Gemini 4 Argon
Proprietary
Kimi K2.6
Open Weight
Release date
Gemini 4 Argon
2026-09-30
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
- Gemini 4 Argon has the higher public score estimate, 64.59 versus 59.22, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.13 vs $0.0595. Cache-heavy agent loop: $0.16 vs $0.091.
- Context tradeoff
- A complete documented context comparison is not available.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Gemini 4 Argon or Kimi K2.6?
Gemini 4 Argon has the higher public score estimate, 64.59 versus 59.22, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Gemini 4 Argon or Kimi K2.6?
Gemini 4 Argon leads the public coding lane, 68.4 to 46.4, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, Gemini 4 Argon or Kimi K2.6?
Gemini 4 Argon scores higher for agentic tasks on the public lane, 63.7 to 44. Gemini 4 Argon 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, Gemini 4 Argon or Kimi K2.6?
For the stated presets, chat costs $0.007 on Gemini 4 Argon and $0.00295 on Kimi K2.6; repository review costs $0.13 and $0.0595; the cache-heavy agent loop costs $0.16 and $0.091. Costs use the listed standard API rates.
Which has the larger context window, Gemini 4 Argon or Kimi K2.6?
A complete documented context-window comparison is not available.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Gemini 4 Argon
API / mo$9,000
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 evidence52 rows
Agentic
AutomationBench
Gemini 4 Argon51.3%SourceKimi K2.6—Not directly comparable
Finance Agent v2
Gemini 4 Argon65.4%SourceKimi K2.6—Not directly comparable
Terminal-Bench 4.0
Gemini 4 Argon57.40%SourceKimi K2.6—Not directly comparable
Agents' Last Exam
Gemini 4 Argon39.5%SourceKimi K2.6—Not directly comparable
OSWorld 2.0
Gemini 4 Argon69.2%SourceKimi K2.64.6%SourceGemini 4 Argon leads this result
CWE-bench v1
Gemini 4 Argon68.0%SourceKimi K2.6—Not directly comparable
Terminal-Bench-Science 0.1 (6x verifier timeout)
Gemini 4 Argon57.6%SourceKimi K2.6—Not directly comparable
Terminal-Bench 2.0
Gemini 4 Argon—Kimi K2.666.7%SourceNot directly comparable
BrowseComp
Gemini 4 Argon—Kimi K2.683.2%SourceNot directly comparable
OSWorld-Verified
Gemini 4 Argon—Kimi K2.673.1%SourceNot directly comparable
Toolathlon
Gemini 4 Argon—Kimi K2.650%SourceNot directly comparable
MCP Atlas
Gemini 4 Argon—Kimi K2.655.9%SourceNot directly comparable
Claw-Eval
Gemini 4 Argon—Kimi K2.662.3%SourceNot directly comparable
DeepSearchQA
Gemini 4 Argon—Kimi K2.692.5%SourceNot directly comparable
WideResearch
Gemini 4 Argon—Kimi K2.680.8%SourceNot directly comparable
Gert Labs
Gemini 4 Argon—Kimi K2.656.82%SourceNot directly comparable
ResearchClawBench
Gemini 4 Argon—Kimi K2.618.0%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 4 Argon—Kimi K2.653.6%SourceNot directly comparable
Coding
DeepSWE
Gemini 4 Argon77.9%SourceKimi K2.6—Not directly comparable
FrontierSWE v2
Gemini 4 Argon55.1%SourceKimi K2.6—Not directly comparable
Vibe Code Bench
Gemini 4 Argon91.90%SourceKimi K2.637.89%SourceGemini 4 Argon leads this result
PostTrainBench v1.1
Gemini 4 Argon45.3%SourceKimi K2.6—Not directly comparable
SWE-bench Verified
Gemini 4 Argon—Kimi K2.680.2%SourceNot directly comparable
LiveCodeBench v6
Gemini 4 Argon—Kimi K2.689.6%SourceNot directly comparable
SWE-bench Pro
Gemini 4 Argon—Kimi K2.658.6%SourceNot directly comparable
SWE Multilingual
Gemini 4 Argon—Kimi K2.676.7%SourceNot directly comparable
SciCode
Gemini 4 Argon—Kimi K2.652.2%SourceNot directly comparable
Terminal-Bench 2.0
Gemini 4 Argon—Kimi K2.666.7%SourceNot directly comparable
cursorBench31
Gemini 4 Argon—Kimi K2.647.6%SourceNot directly comparable
LiveCodeBench (Vals)
Gemini 4 Argon—Kimi K2.686.8%SourceNot directly comparable
SWE-bench (Vals)
Gemini 4 Argon—Kimi K2.676.2%SourceNot directly comparable
Reasoning
Multimodal
Chartography (no tools)
Gemini 4 Argon71.6%SourceKimi K2.6—Not directly comparable
LVBench
Gemini 4 Argon91.7%SourceKimi K2.6—Not directly comparable
MMMU-Pro
Gemini 4 Argon—Kimi K2.679.4%SourceNot directly comparable
MMMU-Pro w/ Python
Gemini 4 Argon—Kimi K2.680.1%SourceNot directly comparable
CharXiv
Gemini 4 Argon—Kimi K2.680.4%SourceNot directly comparable
MathVision
Gemini 4 Argon—Kimi K2.687.4%SourceNot directly comparable
V*
Gemini 4 Argon—Kimi K2.696.9%SourceNot directly comparable
Knowledge
LABBench2
Gemini 4 Argon88.8%SourceKimi K2.6—Not directly comparable
GPQA
Gemini 4 Argon—Kimi K2.690.5%SourceNot directly comparable
GPQA-D
Gemini 4 Argon—Kimi K2.690.5%SourceNot directly comparable
HLE
Gemini 4 Argon—Kimi K2.634.7%SourceNot directly comparable
GPQA Diamond (Vals)
Gemini 4 Argon—Kimi K2.689.1%SourceNot directly comparable
MMLU-Pro (Vals)
Gemini 4 Argon—Kimi K2.687.6%SourceNot directly comparable
Instruction following
Gray Swan IPI (15 attempts)
Gemini 4 Argon0.7%SourceKimi K2.6—Not directly comparable
Math
AIME26
Gemini 4 Argon—Kimi K2.696.4%SourceNot directly comparable
HMMT Feb 2026
Gemini 4 Argon—Kimi K2.692.7%SourceNot directly comparable
MMAnswerBench
Gemini 4 Argon—Kimi K2.686.0%SourceNot directly comparable
FrontierMath v2 (Tiers 1-3)
Gemini 4 Argon—Kimi K2.638.966%SourceNot directly comparable
FrontierMath v2 (Tier 4)
Gemini 4 Argon—Kimi K2.614.580%SourceNot directly comparable
52 public results · 2 shared
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Last updated September 30, 2026