Multimodal
Directional only- Gemini 3 Pro
- 81.1
- Kimi K3
- 78.5
- Weighted basis
- 2 vs 3 rows
- Reading
- Directional only
Gemini 3 Pro vs Kimi K3
Updated August 19, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Kimi K3 has the higher public score estimate, 80.53 versus 67.4, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
4 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
Gemini 3 Pro
Gemini 3 Pro has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Gemini 3 Pro
Gemini 3 Pro has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Kimi K3
Kimi K3 has the lower estimated token cost for this stated workload. Gemini 3 Pro has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Gemini 3 Pro
Gemini 3 Pro 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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
8 Gemini 3 Pro only
4 Shared
36 Kimi K3 only
- Shared results
- 4
- Gemini 3 Pro only
- 8
- Kimi K3 only
- 36
- Like-for-like categories
- 0 / 8
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Agentic
Not comparable- Gemini 3 Pro
- Not measured
- Kimi K3
- 89.5
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Coding
Not comparable- Gemini 3 Pro
- Not measured
- Kimi K3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemini 3 Pro
- 31.1
- Kimi K3
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- Gemini 3 Pro
- Not measured
- Kimi K3
- 61.0
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Math
Not comparable- Gemini 3 Pro
- 32.9
- Kimi K3
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3 Pro
- Not measured
- Kimi K3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 3 Pro
- Not measured
- Kimi K3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Gemini 3 Pro | Kimi K3 | Weighted basis | Reading |
|---|---|---|---|---|
| Multimodal | 81.1 | 78.5 | Directional only2 vs 3 rows | Directional only |
| 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 | 31.1 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Knowledge | Not measured | 61.0 | Not comparable0 vs 2 rows | Not comparable |
| Math | 32.9 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 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.
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.
CharXiv
Multimodal
MMMU-Pro
Multimodal
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 3 Pro
- $0.008
- Fits in one request
- Kimi K3
- $0.0105
- Fits in one request
Gemini 3 Pro has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Gemini 3 Pro
- $0.136
- Fits in one request
- Kimi K3
- $0.195
- Fits in one request
Gemini 3 Pro 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 3 Pro
- $0.56
- Fits in one request
- Cached input priced at the published list-input rate
- Kimi K3
- $0.27
- Fits in one request
Kimi K3 has the lower modeled cost
Gemini 3 Pro has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGemini 3 ProKimi K3
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3 Pro
2M
Kimi K3
1.05M
API model ID
Gemini 3 Pro
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.
Gemini 3 Pro
Not published
Kimi K3
$0.3 per 1M cached input tokens
Documented inputs
Gemini 3 Pro
Not sourced
Kimi K3
Not sourced
Documented outputs
Gemini 3 Pro
Not sourced
Kimi K3
Not sourced
Provider availability
Gemini 3 Pro
Not sourced
Kimi K3
Not sourced
Reasoning profile
Gemini 3 Pro
Non-Reasoning
Kimi K3
Reasoning
Weight access
Gemini 3 Pro
Proprietary
Kimi K3
Pending
License
Gemini 3 Pro
Proprietary
Kimi K3
Pending
Release date
Gemini 3 Pro
2025-11-18
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
- Kimi K3 has the higher public score estimate, 80.53 versus 67.4, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.136 vs $0.195. Cache-heavy agent loop: $0.56 vs $0.27.
- Context tradeoff
- Gemini 3 Pro has the larger documented window (2M).
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence48 rows
Agentic
Gert Labs
Gemini 3 Pro63.23%SourceKimi K3—Not directly comparable
JobBench
Gemini 3 Pro11.4%SourceKimi K352.9%SourceKimi K3 leads this result
Terminal-Bench 2.0
Gemini 3 Pro—Kimi K388.3%SourceNot directly comparable
BrowseComp
Gemini 3 Pro—Kimi K391.2%SourceNot directly comparable
DeepSearchQA
Gemini 3 Pro—Kimi K395.0%SourceNot directly comparable
Toolathlon-Verified
Gemini 3 Pro—Kimi K373.2%SourceNot directly comparable
MCP Atlas
Gemini 3 Pro—Kimi K384.2%SourceNot directly comparable
AutomationBench
Gemini 3 Pro—Kimi K330.8%SourceNot directly comparable
APEX-Agents
Gemini 3 Pro—Kimi K337.6%SourceNot directly comparable
SpreadsheetBench 2
Gemini 3 Pro—Kimi K334.8%SourceNot directly comparable
DECK-Bench
Gemini 3 Pro—Kimi K373.5%SourceNot directly comparable
Coding
Vibe Code Bench
Gemini 3 Pro14.30%SourceKimi K3—Not directly comparable
deepSwe
Gemini 3 Pro—Kimi K367.5%SourceNot directly comparable
cursorBench32
Gemini 3 Pro—Kimi K360.8%SourceNot directly comparable
FrontierSWE
Gemini 3 Pro—Kimi K381.2%SourceNot directly comparable
ProgramBench
Gemini 3 Pro—Kimi K377.8%SourceNot directly comparable
Kimi Code Bench v2
Gemini 3 Pro—Kimi K372.9%SourceNot directly comparable
sweMarathon
Gemini 3 Pro—Kimi K342%SourceNot directly comparable
PostTrain Bench
Gemini 3 Pro—Kimi K336.6%SourceNot directly comparable
MLS-Bench Lite
Gemini 3 Pro—Kimi K348.3%SourceNot directly comparable
VulcanBench v3
Gemini 3 Pro—Kimi K373.9%SourceNot directly comparable
APEX-SWE
Gemini 3 Pro—Kimi K348.0%SourceNot directly comparable
EEBench
Gemini 3 Pro—Kimi K338.3%SourceNot directly comparable
InferenceEval
Gemini 3 Pro—Kimi K339.8%SourceNot directly comparable
KernelBench Internal
Gemini 3 Pro—Kimi K368.5%SourceNot directly comparable
Reasoning
ARC-AGI-2
Gemini 3 Pro31.1%SourceKimi K3—Not directly comparable
Knowledge
Math
Multimodal
MMMU-Pro
Gemini 3 Pro81%SourceKimi K381.6%SourceKimi K3 leads this result
MathVision
Gemini 3 Pro86.6%SourceKimi K394.3%SourceKimi K3 leads this result
VideoMMMU
Gemini 3 Pro87.6%SourceKimi K3—Not directly comparable
ScreenSpot Pro
Gemini 3 Pro72.7%SourceKimi K3—Not directly comparable
CharXiv
Gemini 3 Pro81.4%SourceKimi K391.3%SourceKimi K3 leads this result
V*
Gemini 3 Pro88.0%SourceKimi K3—Not directly comparable
OfficeQA Pro
Gemini 3 Pro—Kimi K363.3%SourceNot directly comparable
MMMU-Pro w/ Python
Gemini 3 Pro—Kimi K383.4%SourceNot directly comparable
CharXiv w/o tools
Gemini 3 Pro—Kimi K384.8%SourceNot directly comparable
MathVision w/ Python
Gemini 3 Pro—Kimi K397.8%SourceNot directly comparable
BabyVision w/ Python
Gemini 3 Pro—Kimi K385.7%SourceNot directly comparable
ZeroBench
Gemini 3 Pro—Kimi K323.0%SourceNot directly comparable
ZeroBench w/ Python
Gemini 3 Pro—Kimi K341.0%SourceNot directly comparable
WorldVQA ForceAnswer
Gemini 3 Pro—Kimi K351.0%SourceNot directly comparable
OmniDocBench
Gemini 3 Pro—Kimi K391.1%SourceNot directly comparable
PerceptionBench
Gemini 3 Pro—Kimi K358.5%SourceNot directly comparable
Frequently asked questions
Which is better, Gemini 3 Pro or Kimi K3?
Kimi K3 has the higher public score estimate, 80.53 versus 67.4, 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 3 Pro 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, Gemini 3 Pro 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, Gemini 3 Pro or Kimi K3?
For the stated presets, chat costs $0.008 on Gemini 3 Pro and $0.0105 on Kimi K3; repository review costs $0.136 and $0.195; the cache-heavy agent loop costs $0.56 and $0.27. Gemini 3 Pro has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Gemini 3 Pro or Kimi K3?
Gemini 3 Pro has the larger documented context window: 2M, compared with 1.05M.
Related comparisons
Last updated August 19, 2026
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