Coding
Like-for-like- Gemini 2.5 Pro
- 63.8
- Kimi K2.5 (Reasoning)
- 76.8
- Weighted basis
- 1 vs 1 rows
- Reading
- Kimi K2.5 (Reasoning) leads
Gemini 2.5 Pro vs Kimi K2.5 (Reasoning)
Updated September 3, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Kimi K2.5 (Reasoning) has the higher public score estimate, 60.49 versus 57.16, 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.
Coding work
Code generation, repair, and software-engineering tasks
Kimi K2.5 (Reasoning)
Kimi K2.5 (Reasoning) leads on the same 1 weighted benchmark row.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Gemini 2.5 Pro
Gemini 2.5 Pro has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Kimi K2.5 (Reasoning)
Kimi K2.5 (Reasoning) 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
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Gemini 2.5 Pro
Gemini 2.5 Pro has the lower estimated token cost for this stated workload. Kimi K2.5 (Reasoning) has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
Kimi K2.5 (Reasoning)
Kimi K2.5 (Reasoning) has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
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.
3 Gemini 2.5 Pro only
4 Shared
5 Kimi K2.5 (Reasoning) only
- Shared results
- 4
- Gemini 2.5 Pro only
- 3
- Kimi K2.5 (Reasoning) only
- 5
- Like-for-like categories
- 1 / 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.
Knowledge
Directional only- Gemini 2.5 Pro
- 27.4
- Kimi K2.5 (Reasoning)
- 87.2
- Weighted basis
- 2 vs 2 rows
- Reading
- Directional only
Agentic
Not comparable- Gemini 2.5 Pro
- Not measured
- Kimi K2.5 (Reasoning)
- 55.0
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemini 2.5 Pro
- Not measured
- Kimi K2.5 (Reasoning)
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- Gemini 2.5 Pro
- 11.6
- Kimi K2.5 (Reasoning)
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 2.5 Pro
- Not measured
- Kimi K2.5 (Reasoning)
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 2.5 Pro
- Not measured
- Kimi K2.5 (Reasoning)
- 78.5
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 2.5 Pro
- Not measured
- Kimi K2.5 (Reasoning)
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Gemini 2.5 Pro | Kimi K2.5 (Reasoning) | Weighted basis | Reading |
|---|---|---|---|---|
| Coding | 63.8 | 76.8 | Like-for-like1 vs 1 rows | Kimi K2.5 (Reasoning) leads |
| Knowledge | 27.4 | 87.2 | Directional only2 vs 2 rows | Directional only |
| Agentic | Not measured | 55.0 | Not comparable0 vs 2 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Math | 11.6 | Not measured | Not comparable2 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 1 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.
SWE-bench Verified
Coding
Gemini 2.5 Pro: 63.8%Kimi K2.5 (Reasoning): 76.8%Normalized gap 13.0Gemini 2.5 Pro source Kimi K2.5 (Reasoning) sourceGPQA
Knowledge
Gemini 2.5 Pro: 83%Kimi K2.5 (Reasoning): 87.6%Normalized gap 4.6Gemini 2.5 Pro source Kimi K2.5 (Reasoning) source
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 2.5 Pro
- $0.00625
- Fits in one request
- Kimi K2.5 (Reasoning)
- $0.0021
- Fits in one request
Kimi K2.5 (Reasoning) has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Gemini 2.5 Pro
- $0.0925
- Fits in one request
- Kimi K2.5 (Reasoning)
- $0.039
- Fits in one request
Kimi K2.5 (Reasoning) 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 2.5 Pro
- $0.15
- Fits in one request
- Kimi K2.5 (Reasoning)
- $0.162
- Fits in one request
- Cached input priced at the published list-input rate
Gemini 2.5 Pro has the lower modeled cost
Kimi K2.5 (Reasoning) 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 2.5 ProKimi K2.5 (Reasoning)
Context window
Maximum documented context; output-token limits may be lower.
Gemini 2.5 Pro
Kimi K2.5 (Reasoning)
256K
API model ID
Gemini 2.5 Pro
gemini-2.5-pro
Google Gemini API pricingKimi K2.5 (Reasoning)
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 2.5 Pro
$0.125 per 1M cached input tokens
Google Gemini API pricingKimi K2.5 (Reasoning)
Not published
Documented inputs
Gemini 2.5 Pro
Not sourced
Kimi K2.5 (Reasoning)
Not sourced
Documented outputs
Gemini 2.5 Pro
Not sourced
Kimi K2.5 (Reasoning)
Not sourced
Provider availability
Gemini 2.5 Pro
Not sourced
Kimi K2.5 (Reasoning)
Not sourced
Reasoning profile
Gemini 2.5 Pro
Non-Reasoning
Kimi K2.5 (Reasoning)
Reasoning
Weight access
Gemini 2.5 Pro
Proprietary
Kimi K2.5 (Reasoning)
Proprietary
License
Gemini 2.5 Pro
Proprietary
Kimi K2.5 (Reasoning)
Proprietary
Release date
Gemini 2.5 Pro
2025-03-01
Kimi K2.5 (Reasoning)
2026-02-01
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
- Kimi K2.5 (Reasoning) has the higher public score estimate, 60.49 versus 57.16, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.0925 vs $0.039. Cache-heavy agent loop: $0.15 vs $0.162.
- Context tradeoff
- Gemini 2.5 Pro has the larger documented window (1M).
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
Gert Labs
Shared sourceGemini 2.5 Pro42.01%Kimi K2.5 (Reasoning)32.58%Gemini 2.5 Pro leads this result
Terminal-Bench 2.0
Gemini 2.5 Pro—Kimi K2.5 (Reasoning)50.8%SourceNot directly comparable
BrowseComp
Gemini 2.5 Pro—Kimi K2.5 (Reasoning)60.6%SourceNot directly comparable
Coding
SWE-bench Verified
Gemini 2.5 Pro63.8%SourceKimi K2.5 (Reasoning)76.8%SourceKimi K2.5 (Reasoning) leads this result
Vibe Code Bench
Shared sourceGemini 2.5 Pro0.40%Kimi K2.5 (Reasoning)17.54%Kimi K2.5 (Reasoning) leads this result
Knowledge
Math
Multimodal
MMMU-Pro
Gemini 2.5 Pro—Kimi K2.5 (Reasoning)78.5%SourceNot directly comparable
Frequently asked questions
Which is better, Gemini 2.5 Pro or Kimi K2.5 (Reasoning)?
Kimi K2.5 (Reasoning) has the higher public score estimate, 60.49 versus 57.16, 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 2.5 Pro or Kimi K2.5 (Reasoning)?
Kimi K2.5 (Reasoning) leads the like-for-like coding comparison across 1 shared weighted benchmark row.
Which is better for agentic tasks, Gemini 2.5 Pro or Kimi K2.5 (Reasoning)?
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 2.5 Pro or Kimi K2.5 (Reasoning)?
For the stated presets, chat costs $0.00625 on Gemini 2.5 Pro and $0.0021 on Kimi K2.5 (Reasoning); repository review costs $0.0925 and $0.039; the cache-heavy agent loop costs $0.15 and $0.162. Kimi K2.5 (Reasoning) has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Gemini 2.5 Pro or Kimi K2.5 (Reasoning)?
Gemini 2.5 Pro has the larger documented context window: 1M, compared with 256K.
Related comparisons
Last updated September 3, 2026
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