Long documents
Prompts that approach the documented context limit
Gemini 3.1 Pro
Gemini 3.1 Pro has the larger documented context window.
Confidence: documented
Gemini 3.1 Pro vs Kimi K2.5 (Reasoning)
Updated October 17, 2026. Rank says Gemini 3.1 Pro is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
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Decision reading
Gemini 3.1 Pro has the higher public point estimate, 64.7 versus 56.09. Their conditional score ranges overlap. These ranges do not establish rank confidence. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Model B
Moonshot AI
56.09/100
Estimated · Public rank #63
Conditional range 41.7–70.4
- Shared results
- 4
- Gemini 3.1 Pro only
- 26
- Kimi K2.5 (Reasoning) only
- 11
- Like-for-like categories
- 1 / 8
Estimated: Gemini 3.1 Pro and Kimi K2.5 (Reasoning). Conditional ranges do not establish rank confidence.How the comparison works
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.
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-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Kimi K2.5 (Reasoning)
Kimi K2.5 (Reasoning) 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
Show secondary and unsupported calls
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-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Kimi K2.5 (Reasoning) is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Kimi K2.5 (Reasoning) is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Which one for a specific job
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.
39.2Gemini 3.1 Pro—Kimi K2.5 (Reasoning)
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
26 Gemini 3.1 Pro only
4 Shared
11 Kimi K2.5 (Reasoning) only
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
MMMU-ProMultimodal
Normalized gap 5.4Gemini 3.1 Pro:83.9%Kimi K2.5 (Reasoning):78.5%CharXivMultimodal
Normalized gap 2.7Gemini 3.1 Pro:80.2%Kimi K2.5 (Reasoning):77.5%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
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.
Multimodal
Like-for-like- Gemini 3.1 Pro
- 80.1
- #17/54
- Kimi K2.5 (Reasoning)
- 69.0
- #33/54
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Gemini 3.1 Pro leads
Agentic
Directional only- Gemini 3.1 Pro
- 38.4
- Supported · #70/128
- Kimi K2.5 (Reasoning)
- 38.3
- Estimated · #71/128
- Basis
- BenchAlign v5.8 lane · 7 vs 4 public rows
- Reading
- Directional only
Knowledge
Directional only- Gemini 3.1 Pro
- 67.6
- Supported · #26/184
- Kimi K2.5 (Reasoning)
- 48.8
- Estimated · #85/184
- Basis
- BenchAlign v5.8 lane · 6 vs 2 public rows
- Reading
- Directional only
Coding
Not comparable- Gemini 3.1 Pro
- 39.2
- Supported · #67/150
- Kimi K2.5 (Reasoning)
- Not ranked
- Basis
- BenchAlign v5.8 lane · 6 vs 4 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemini 3.1 Pro
- 58.2
- Unranked · 2 rankable rows
- Kimi K2.5 (Reasoning)
- 67.1
- Unranked · 1 rankable row
- Basis
- Provisional lane · 2 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3.1 Pro
- Not ranked
- Kimi K2.5 (Reasoning)
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 3.1 Pro
- Not ranked
- Kimi K2.5 (Reasoning)
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 3.1 Pro
- 65.0
- Unranked · 2 rankable rows
- Kimi K2.5 (Reasoning)
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemini 3.1 Pro | Kimi K2.5 (Reasoning) | Basis | Reading |
|---|---|---|---|---|
| Multimodal | 80.1#17/54 | 69.0#33/54 | Like-for-likeProvisional lane · 2 vs 2 weighted rows | Gemini 3.1 Pro leads |
| Agentic | 38.4Supported · #70/128 | 38.3Estimated · #71/128 | Directional onlyBenchAlign v5.8 lane · 7 vs 4 public rows | Directional only |
| Knowledge | 67.6Supported · #26/184 | 48.8Estimated · #85/184 | Directional onlyBenchAlign v5.8 lane · 6 vs 2 public rows | Directional only |
| Coding | 39.2Supported · #67/150 | Not ranked | Not comparableBenchAlign v5.8 lane · 6 vs 4 public rows | Not comparable |
| Reasoning | 58.2Unranked · 2 rankable rows | 67.1Unranked · 1 rankable row | Not comparableProvisional lane · 2 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 65.0Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 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.
Supported evidence per lane · bars run 0–100Methodology
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 3.1 Pro
- $0.008
- 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 3.1 Pro
- $0.136
- 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 3.1 Pro
- $0.2
- Fits in one request
- Kimi K2.5 (Reasoning)
- $0.162
- Fits in one request
- Cached input priced at the published list-input rate
Kimi K2.5 (Reasoning) has the lower modeled cost
Kimi K2.5 (Reasoning) has no published cached-input rate, so cached tokens use its listed input rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGemini 3.1 ProKimi K2.5 (Reasoning)
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3.1 Pro
Kimi K2.5 (Reasoning)
256K
API model ID
Gemini 3.1 Pro
gemini-3.1-pro-preview
Google Gemini 3.1 Pro Preview model documentationKimi 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 3.1 Pro
$0.2 per 1M cached input tokens
Google Gemini API pricingKimi K2.5 (Reasoning)
Not published
Documented inputs
Gemini 3.1 Pro
text, image, video, audio, pdf
Google Gemini 3.1 Pro Preview model documentationKimi K2.5 (Reasoning)
Not sourced
Documented outputs
Gemini 3.1 Pro
Kimi K2.5 (Reasoning)
Not sourced
Provider availability
Gemini 3.1 Pro
Preview · Gemini API, Google AI Studio
Google Gemini 3.1 Pro Preview model documentationKimi K2.5 (Reasoning)
Not sourced
Reasoning profile
Gemini 3.1 Pro
Reasoning
Kimi K2.5 (Reasoning)
Reasoning
Weight access
Gemini 3.1 Pro
Proprietary
Kimi K2.5 (Reasoning)
Proprietary
License
Gemini 3.1 Pro
Proprietary
Kimi K2.5 (Reasoning)
Proprietary
Release date
Gemini 3.1 Pro
2026-02-19
Kimi K2.5 (Reasoning)
2026-01-27
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 3.1 Pro has the higher public point estimate, 64.7 versus 56.09. Their conditional score ranges overlap. These ranges do not establish rank confidence.
- Workload cost
- Repository review: $0.136 vs $0.039. Cache-heavy agent loop: $0.2 vs $0.162.
- Context tradeoff
- Gemini 3.1 Pro has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Gemini 3.1 Pro or Kimi K2.5 (Reasoning)?
Gemini 3.1 Pro has the higher public point estimate, 64.7 versus 56.09. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Gemini 3.1 Pro or Kimi K2.5 (Reasoning)?
Kimi K2.5 (Reasoning) is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Gemini 3.1 Pro or Kimi K2.5 (Reasoning)?
Gemini 3.1 Pro scores higher for agentic tasks on the public lane, 38.4 to 38.3. Kimi K2.5 (Reasoning) 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.
Which costs less, Gemini 3.1 Pro or Kimi K2.5 (Reasoning)?
For the stated presets, chat costs $0.008 on Gemini 3.1 Pro and $0.0021 on Kimi K2.5 (Reasoning); repository review costs $0.136 and $0.039; the cache-heavy agent loop costs $0.2 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 3.1 Pro or Kimi K2.5 (Reasoning)?
Gemini 3.1 Pro has the larger documented context window: 1M, compared with 256K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence41 rows
Agentic
Claw-Eval
Gemini 3.1 Pro57.8%SourceKimi K2.5 (Reasoning)—Not directly comparable
DeepSearchQA
Gemini 3.1 Pro69.7%SourceKimi K2.5 (Reasoning)—Not directly comparable
τ²-bench results
Gemini 3.1 Pro95.6%SourceKimi K2.5 (Reasoning)—Not directly comparable
Gert Labs
Shared sourceGemini 3.1 Pro56.87%Kimi K2.5 (Reasoning)32.58%Gemini 3.1 Pro leads this result
ResearchClawBench
Gemini 3.1 Pro13.3%SourceKimi K2.5 (Reasoning)—Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.1 Pro70.8%SourceKimi K2.5 (Reasoning)—Not directly comparable
MazeBench
Gemini 3.1 Pro1%SourceKimi K2.5 (Reasoning)—Not directly comparable
Terminal-Bench 2.0
Gemini 3.1 Pro—Kimi K2.5 (Reasoning)50.8%SourceNot directly comparable
BrowseComp
Gemini 3.1 Pro—Kimi K2.5 (Reasoning)60.6%SourceNot directly comparable
OSWorld-Verified
Gemini 3.1 Pro—Kimi K2.5 (Reasoning)63.3%SourceNot directly comparable
Coding
LiveCodeBench Pro
Gemini 3.1 Pro82.9%SourceKimi K2.5 (Reasoning)—Not directly comparable
React Native Evals
Gemini 3.1 Pro78.9%SourceKimi K2.5 (Reasoning)—Not directly comparable
Vibe Code Bench
Shared sourceGemini 3.1 Pro32.03%Kimi K2.5 (Reasoning)17.54%Gemini 3.1 Pro leads this result
LiveCodeBench (Vals)
Gemini 3.1 Pro88.5%SourceKimi K2.5 (Reasoning)—Not directly comparable
SWE-bench (Vals)
Gemini 3.1 Pro78.8%SourceKimi K2.5 (Reasoning)—Not directly comparable
PostTrainBench v1.1
Gemini 3.1 Pro22.0%SourceKimi K2.5 (Reasoning)—Not directly comparable
SWE-bench Verified
Gemini 3.1 Pro—Kimi K2.5 (Reasoning)76.8%SourceNot directly comparable
LiveCodeBench v6
Gemini 3.1 Pro—Kimi K2.5 (Reasoning)85.0%SourceNot directly comparable
Terminal-Bench 2.0
Gemini 3.1 Pro—Kimi K2.5 (Reasoning)50.8%SourceNot directly comparable
Reasoning
Multimodal
MMMU-Pro
Gemini 3.1 Pro83.9%SourceKimi K2.5 (Reasoning)78.5%SourceGemini 3.1 Pro leads this result
CharXiv
Gemini 3.1 Pro80.2%SourceKimi K2.5 (Reasoning)77.5%SourceGemini 3.1 Pro leads this result
ERQA
Gemini 3.1 Pro69.4%SourceKimi K2.5 (Reasoning)—Not directly comparable
SimpleVQA
Gemini 3.1 Pro72.4%SourceKimi K2.5 (Reasoning)—Not directly comparable
ScreenSpot Pro
Gemini 3.1 Pro84.4%SourceKimi K2.5 (Reasoning)—Not directly comparable
ZeroBench
Gemini 3.1 Pro29.0%SourceKimi K2.5 (Reasoning)—Not directly comparable
MedXpertQA (MM)
Gemini 3.1 Pro81.3%SourceKimi K2.5 (Reasoning)—Not directly comparable
Knowledge
GPQA-D
Gemini 3.1 Pro94.3%SourceKimi K2.5 (Reasoning)—Not directly comparable
HLE w/o tools
Gemini 3.1 Pro45.4%SourceKimi K2.5 (Reasoning)—Not directly comparable
HealthBench Hard
Gemini 3.1 Pro20.6%SourceKimi K2.5 (Reasoning)—Not directly comparable
MedXpertQA (Text)
Gemini 3.1 Pro71.5%SourceKimi K2.5 (Reasoning)—Not directly comparable
GPQA Diamond (Vals)
Gemini 3.1 Pro95.5%SourceKimi K2.5 (Reasoning)—Not directly comparable
MMLU-Pro (Vals)
Gemini 3.1 Pro91.0%SourceKimi K2.5 (Reasoning)—Not directly comparable
GPQA
Gemini 3.1 Pro—Kimi K2.5 (Reasoning)87.6%SourceNot directly comparable
MMLU-Pro
Gemini 3.1 Pro—Kimi K2.5 (Reasoning)87.1%SourceNot directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Gemini 3.1 Pro59.649%SourceKimi K2.5 (Reasoning)—Not directly comparable
FrontierMath v2 (Tier 4)
Gemini 3.1 Pro26.829%SourceKimi K2.5 (Reasoning)—Not directly comparable
AIME 2025
Gemini 3.1 Pro—Kimi K2.5 (Reasoning)96.1%SourceNot directly comparable
HMMT Feb 2025
Gemini 3.1 Pro—Kimi K2.5 (Reasoning)95.4%SourceNot directly comparable
41 public results · 4 shared
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Last updated October 17, 2026