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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 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
Kimi K2.6
Kimi K2.6 has the higher public coding point estimate, 43.9 to 15.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
1K fresh input + 500 output tokens
Mistral Large 3
Mistral Large 3 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 K2.6
Kimi K2.6 has the lower estimated token cost for this stated workload. Mistral Large 3 has no published cached-input rate, so cached tokens use its listed input rate.
50K fresh input + 3K output tokens
Mistral Large 3
Mistral Large 3 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
Not enough matched evidence
Mistral Large 3 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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
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.
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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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.
| Category | Kimi K2.6 | Mistral Large 3 | Basis | Reading |
|---|---|---|---|---|
| Coding | 43.9Supported · #54/146 | 15.7Supported · #131/146 | Like-for-likeBenchAlign v5.8 lane · 10 vs 0 public rows | Kimi K2.6 leads |
| Agentic | 43.7Supported · #57/123 | 10.5Estimated · #116/123 | Directional onlyBenchAlign v5.8 lane · 12 vs 0 public rows | Directional only |
| Knowledge | 59.8Supported · #45/177 | 31.8Estimated · #141/177 | Directional onlyBenchAlign v5.8 lane · 5 vs 0 public rows | Directional only |
| Reasoning | Not ranked | 50.7Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 73.8#27/54 | 46.6Unranked · 1 rankable row | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 41.5#102/127 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 72.2#1/7 | Not ranked | Not comparableProvisional lane · 4 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
Mistral Large 3 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Mistral Large 3 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Kimi K2.6 has the lower modeled cost
Mistral Large 3 has no published cached-input rate, so cached tokens use its listed input rate.
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.
Kimi K2.6
256K
Mistral Large 3
256K
Kimi K2.6
Not sourced
Mistral Large 3
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Kimi K2.6
$0.16 per 1M cached input tokens
Mistral Large 3
Not published
Kimi K2.6
Not sourced
Mistral Large 3
Not sourced
Kimi K2.6
Not sourced
Mistral Large 3
Not sourced
Kimi K2.6
Not sourced
Mistral Large 3
Not sourced
Kimi K2.6
Reasoning
Mistral Large 3
Non-Reasoning
Kimi K2.6
Open Weight
Mistral Large 3
Proprietary
Kimi K2.6
Open Weight
Mistral Large 3
Proprietary
Kimi K2.6
2026-04-20
Mistral Large 3
2025-12-02
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Kimi K2.6 has the higher public coding point estimate, 43.9 to 15.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Kimi K2.6 scores higher for agentic tasks on the public lane, 43.7 to 10.5. Mistral Large 3 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.
For the stated presets, chat costs $0.00295 on Kimi K2.6 and $0.00125 on Mistral Large 3; repository review costs $0.0595 and $0.0295; the cache-heavy agent loop costs $0.091 and $0.125. Mistral Large 3 has no published cached-input rate, so cached tokens use its listed input rate.
Both models list the same context window, 256K.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Not directly comparable
BrowseComp
Not directly comparable
OSWorld-Verified
Not directly comparable
Toolathlon
Not directly comparable
MCP Atlas
Not directly comparable
Claw-Eval
Not directly comparable
DeepSearchQA
Not directly comparable
WideResearch
Not directly comparable
Gert Labs
Not directly comparable
ResearchClawBench
Not directly comparable
OSWorld 2.0
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
SWE-bench Verified
Not directly comparable
LiveCodeBench v6
Not directly comparable
SWE-bench Pro
Not directly comparable
SWE Multilingual
Not directly comparable
SciCode
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Not directly comparable
cursorBench31
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
MMMU-Pro
Not directly comparable
MMMU-Pro w/ Python
Not directly comparable
CharXiv
Not directly comparable
MathVision
Not directly comparable
V*
Not directly comparable
GPQA
Not directly comparable
GPQA-D
Not directly comparable
HLE
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
AIME26
Not directly comparable
HMMT Feb 2026
Not directly comparable
MMAnswerBench
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Not directly comparable
FrontierMath v2 (Tier 4)
Not directly comparable
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Last updated October 10, 2026
