Agentic
Not comparable- DeepSeek-R1
- Not measured
- Kimi K3
- 89.5
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
DeepSeek-R1 vs Kimi K3
Updated August 19, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
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 based on different benchmark sets are marked directional and do not name a winner.
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.
Long documents
Prompts that approach the documented context limit
Kimi K3
Kimi K3 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
DeepSeek-R1
DeepSeek-R1 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
DeepSeek-R1
DeepSeek-R1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Cache-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. DeepSeek-R1 does not fit this workload in one request.
Confidence: listed-rates
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
40 Kimi K3 only
- Shared results
- 0
- DeepSeek-R1 only
- 0
- Kimi K3 only
- 40
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Coding
Not comparable- DeepSeek-R1
- Not measured
- Kimi K3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- DeepSeek-R1
- Not measured
- Kimi K3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- DeepSeek-R1
- Not measured
- Kimi K3
- 61.0
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Math
Not comparable- DeepSeek-R1
- Not measured
- Kimi K3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- DeepSeek-R1
- Not measured
- Kimi K3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- DeepSeek-R1
- Not measured
- Kimi K3
- 78.5
- Weighted basis
- 0 vs 3 rows
- Reading
- Not comparable
Instruction following
Not comparable- DeepSeek-R1
- Not measured
- Kimi K3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | DeepSeek-R1 | Kimi K3 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | Not measured | 89.5 | Not comparable0 vs 2 rows | Not comparable |
| Coding | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | 61.0 | Not comparable0 vs 2 rows | Not comparable |
| Math | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 78.5 | Not comparable0 vs 3 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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.
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
- DeepSeek-R1
- $0.00165
- Fits in one request
- Kimi K3
- $0.0105
- Fits in one request
DeepSeek-R1 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- DeepSeek-R1
- $0.03407
- Fits in one request
- Kimi K3
- $0.195
- Fits in one request
DeepSeek-R1 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- DeepSeek-R1
- $0.0609
- Does not fit in one request
- Kimi K3
- $0.27
- Fits in one request
DeepSeek-R1 does not fit this workload in one request.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationDeepSeek-R1Kimi K3
Context window
Maximum documented context; output-token limits may be lower.
DeepSeek-R1
128K
Kimi K3
1.05M
API model ID
DeepSeek-R1
Not sourced
Kimi K3
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
DeepSeek-R1
$0.14 per 1M cached input tokens
Kimi K3
$0.3 per 1M cached input tokens
Documented inputs
DeepSeek-R1
Not sourced
Kimi K3
Not sourced
Documented outputs
DeepSeek-R1
Not sourced
Kimi K3
Not sourced
Provider availability
DeepSeek-R1
Not sourced
Kimi K3
Not sourced
Reasoning profile
DeepSeek-R1
Reasoning
Kimi K3
Reasoning
Weight access
DeepSeek-R1
Open Weight
Kimi K3
Pending
License
DeepSeek-R1
Open Weight
Kimi K3
Pending
Release date
DeepSeek-R1
2025-01-20
Kimi K3
2026-07-16
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- Repository review: $0.03407 vs $0.195. Cache-heavy agent loop: $0.0609 vs $0.27.
- Context tradeoff
- Kimi K3 has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
DeepSeek-R1
API / mo$2,055
Self-host / mo$18,221
Break-even583M/day
Kimi K3
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence40 rows
Agentic
Terminal-Bench 2.0
DeepSeek-R1—Kimi K388.3%SourceNot directly comparable
BrowseComp
DeepSeek-R1—Kimi K391.2%SourceNot directly comparable
DeepSearchQA
DeepSeek-R1—Kimi K395.0%SourceNot directly comparable
Toolathlon-Verified
DeepSeek-R1—Kimi K373.2%SourceNot directly comparable
MCP Atlas
DeepSeek-R1—Kimi K384.2%SourceNot directly comparable
AutomationBench
DeepSeek-R1—Kimi K330.8%SourceNot directly comparable
JobBench
DeepSeek-R1—Kimi K352.9%SourceNot directly comparable
APEX-Agents
DeepSeek-R1—Kimi K337.6%SourceNot directly comparable
SpreadsheetBench 2
DeepSeek-R1—Kimi K334.8%SourceNot directly comparable
DECK-Bench
DeepSeek-R1—Kimi K373.5%SourceNot directly comparable
Coding
deepSwe
DeepSeek-R1—Kimi K367.5%SourceNot directly comparable
cursorBench32
DeepSeek-R1—Kimi K360.8%SourceNot directly comparable
FrontierSWE
DeepSeek-R1—Kimi K381.2%SourceNot directly comparable
ProgramBench
DeepSeek-R1—Kimi K377.8%SourceNot directly comparable
Kimi Code Bench v2
DeepSeek-R1—Kimi K372.9%SourceNot directly comparable
sweMarathon
DeepSeek-R1—Kimi K342%SourceNot directly comparable
PostTrain Bench
DeepSeek-R1—Kimi K336.6%SourceNot directly comparable
MLS-Bench Lite
DeepSeek-R1—Kimi K348.3%SourceNot directly comparable
VulcanBench v3
DeepSeek-R1—Kimi K373.9%SourceNot directly comparable
APEX-SWE
DeepSeek-R1—Kimi K348.0%SourceNot directly comparable
EEBench
DeepSeek-R1—Kimi K338.3%SourceNot directly comparable
InferenceEval
DeepSeek-R1—Kimi K339.8%SourceNot directly comparable
KernelBench Internal
DeepSeek-R1—Kimi K368.5%SourceNot directly comparable
Knowledge
Multimodal
OfficeQA Pro
DeepSeek-R1—Kimi K363.3%SourceNot directly comparable
MMMU-Pro
DeepSeek-R1—Kimi K381.6%SourceNot directly comparable
MMMU-Pro w/ Python
DeepSeek-R1—Kimi K383.4%SourceNot directly comparable
CharXiv w/o tools
DeepSeek-R1—Kimi K384.8%SourceNot directly comparable
CharXiv
DeepSeek-R1—Kimi K391.3%SourceNot directly comparable
MathVision
DeepSeek-R1—Kimi K394.3%SourceNot directly comparable
MathVision w/ Python
DeepSeek-R1—Kimi K397.8%SourceNot directly comparable
BabyVision w/ Python
DeepSeek-R1—Kimi K385.7%SourceNot directly comparable
ZeroBench
DeepSeek-R1—Kimi K323.0%SourceNot directly comparable
ZeroBench w/ Python
DeepSeek-R1—Kimi K341.0%SourceNot directly comparable
WorldVQA ForceAnswer
DeepSeek-R1—Kimi K351.0%SourceNot directly comparable
OmniDocBench
DeepSeek-R1—Kimi K391.1%SourceNot directly comparable
PerceptionBench
DeepSeek-R1—Kimi K358.5%SourceNot directly comparable
Frequently asked questions
Which is better, DeepSeek-R1 or Kimi K3?
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.
Which is better for coding, DeepSeek-R1 or Kimi K3?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, DeepSeek-R1 or Kimi K3?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, DeepSeek-R1 or Kimi K3?
For the stated presets, chat costs $0.00164 on DeepSeek-R1 and $0.0105 on Kimi K3; repository review costs $0.03407 and $0.195; the cache-heavy agent loop costs $0.0609 and $0.27. DeepSeek-R1 does not fit this workload in one request.
Which has the larger context window, DeepSeek-R1 or Kimi K3?
Kimi K3 has the larger documented context window: 1.05M, compared with 128K.
Related comparisons
Last updated August 19, 2026
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