Math
Like-for-like- Gemini 3 Pro
- 32.9
- Kimi K2
- 16.1
- Weighted basis
- 2 vs 2 rows
- Reading
- Gemini 3 Pro leads
Gemini 3 Pro vs Kimi K2
Updated September 3, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Gemini 3 Pro has the higher public score, 67.41 versus 26.28, and the 90% score intervals do not overlap.
2 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
Kimi K2
Kimi K2 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
Kimi K2 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. Kimi K2 does not fit this workload in one request. Gemini 3 Pro has no published cached-input rate, so cached tokens use its listed input rate. Kimi K2 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
10 Gemini 3 Pro only
2 Shared
- Shared results
- 2
- Gemini 3 Pro only
- 10
- Kimi K2 only
- 0
- Like-for-like categories
- 1 / 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.
Agentic
Not comparable- Gemini 3 Pro
- Not measured
- Kimi K2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Coding
Not comparable- Gemini 3 Pro
- Not measured
- Kimi K2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemini 3 Pro
- 31.1
- Kimi K2
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- Gemini 3 Pro
- Not measured
- Kimi K2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3 Pro
- Not measured
- Kimi K2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 3 Pro
- 81.1
- Kimi K2
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 3 Pro
- Not measured
- Kimi K2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Gemini 3 Pro | Kimi K2 | Weighted basis | Reading |
|---|---|---|---|---|
| Math | 32.9 | 16.1 | Like-for-like2 vs 2 rows | Gemini 3 Pro leads |
| Agentic | Not measured | Not measured | Not comparable0 vs 0 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 | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | 81.1 | Not measured | Not comparable2 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.
FrontierMath v2 (Tier 4)
Math
FrontierMath v2 (Tiers 1-3)
Math
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 K2
- $0.00185
- Fits in one request
Kimi K2 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 K2
- $0.0375
- Fits in one request
Kimi K2 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 K2
- $0.157
- Does not fit in one request
- Cached input priced at the published list-input rate
Kimi K2 does not fit this workload in one request. Gemini 3 Pro has no published cached-input rate, so cached tokens use its listed input rate. Kimi K2 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 K2
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3 Pro
2M
Kimi K2
128K
API model ID
Gemini 3 Pro
Not sourced
Kimi K2
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 K2
Not published
Documented inputs
Gemini 3 Pro
Not sourced
Kimi K2
Not sourced
Documented outputs
Gemini 3 Pro
Not sourced
Kimi K2
Not sourced
Provider availability
Gemini 3 Pro
Not sourced
Kimi K2
Not sourced
Reasoning profile
Gemini 3 Pro
Non-Reasoning
Kimi K2
Non-Reasoning
Weight access
Gemini 3 Pro
Proprietary
Kimi K2
Proprietary
License
Gemini 3 Pro
Proprietary
Kimi K2
Proprietary
Release date
Gemini 3 Pro
2025-11-18
Kimi K2
2025-07-01
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 3 Pro has the higher public score, 67.41 versus 26.28, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.136 vs $0.0375. Cache-heavy agent loop: $0.56 vs $0.157.
- 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 evidence12 rows
Agentic
Coding
Vibe Code Bench
Gemini 3 Pro14.30%SourceKimi K2—Not directly comparable
Reasoning
ARC-AGI-2
Gemini 3 Pro31.1%SourceKimi K2—Not directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceGemini 3 Pro37.600%Kimi K221.404%Gemini 3 Pro leads this result
FrontierMath v2 (Tier 4)
Shared sourceGemini 3 Pro18.750%Kimi K20.000%Gemini 3 Pro leads this result
Multimodal
MMMU-Pro
Gemini 3 Pro81%SourceKimi K2—Not directly comparable
MathVision
Gemini 3 Pro86.6%SourceKimi K2—Not directly comparable
VideoMMMU
Gemini 3 Pro87.6%SourceKimi K2—Not directly comparable
ScreenSpot Pro
Gemini 3 Pro72.7%SourceKimi K2—Not directly comparable
CharXiv
Gemini 3 Pro81.4%SourceKimi K2—Not directly comparable
V*
Gemini 3 Pro88.0%SourceKimi K2—Not directly comparable
Frequently asked questions
Which is better, Gemini 3 Pro or Kimi K2?
Gemini 3 Pro has the higher public score, 67.41 versus 26.28, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, Gemini 3 Pro or Kimi K2?
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 K2?
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 K2?
For the stated presets, chat costs $0.008 on Gemini 3 Pro and $0.00185 on Kimi K2; repository review costs $0.136 and $0.0375; the cache-heavy agent loop costs $0.56 and $0.157. Kimi K2 does not fit this workload in one request. Gemini 3 Pro has no published cached-input rate, so cached tokens use its listed input rate. Kimi K2 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 K2?
Gemini 3 Pro has the larger documented context window: 2M, compared with 128K.
Related comparisons
Last updated September 3, 2026
Watch Gemini 3 Pro vs Kimi K2
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.