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
Gemini 2.5 Pro vs K-Exaone
Updated November 6, 2026. Rank says Gemini 2.5 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 2.5 Pro has the higher public point estimate, 49.07 versus 38. Their conditional score ranges overlap. These ranges do not establish rank confidence. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 1
- Gemini 2.5 Pro only
- 8
- K-Exaone only
- 0
- Like-for-like categories
- 0 / 8
Supported: Gemini 2.5 Pro · Estimated: K-Exaone. 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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
K-Exaone is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
K-Exaone is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedChat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesRepository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
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.
23.1Gemini 2.5 Pro25.9K-Exaone
Directional only · BenchAlign v5.8
K-Exaone has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
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.
8 Gemini 2.5 Pro only
1 Shared
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.
SWE-bench VerifiedCoding
Normalized gap 14.4Gemini 2.5 Pro:63.8%K-Exaone:49.4%
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.
Coding
Directional only- Gemini 2.5 Pro
- 23.1
- Supported · #127/157
- K-Exaone
- 25.9
- Estimated · #122/157
- Basis
- BenchAlign v5.8 lane · 3 vs 1 public rows
- Reading
- Directional only
Knowledge
Directional only- Gemini 2.5 Pro
- 45.1
- Supported · #100/178
- K-Exaone
- 35.8
- Estimated · #134/178
- Basis
- BenchAlign v5.8 lane · 2 vs 0 public rows
- Reading
- Directional only
Agentic
Not comparable- Gemini 2.5 Pro
- 29.3
- Estimated · #106/131
- K-Exaone
- Not ranked
- Basis
- BenchAlign v5.8 lane · 1 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemini 2.5 Pro
- 70.7
- Unranked · 2 rankable rows
- K-Exaone
- 65.4
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 2.5 Pro
- 75.0
- Unranked · 1 rankable row
- K-Exaone
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 2.5 Pro
- Not ranked
- K-Exaone
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 2.5 Pro
- 57.8
- #90/157
- K-Exaone
- 78.7
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 2.5 Pro
- 33.4
- Unranked · 3 rankable rows
- K-Exaone
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemini 2.5 Pro | K-Exaone | Basis | Reading |
|---|---|---|---|---|
| Coding | 23.1Supported · #127/157 | 25.9Estimated · #122/157 | Directional onlyBenchAlign v5.8 lane · 3 vs 1 public rows | Directional only |
| Knowledge | 45.1Supported · #100/178 | 35.8Estimated · #134/178 | Directional onlyBenchAlign v5.8 lane · 2 vs 0 public rows | Directional only |
| Agentic | 29.3Estimated · #106/131 | Not ranked | Not comparableBenchAlign v5.8 lane · 1 vs 0 public rows | Not comparable |
| Reasoning | 70.7Unranked · 2 rankable rows | 65.4Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 75.0Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 57.8#90/157 | 78.7Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 33.4Unranked · 3 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 2.5 Pro
- $0.00625
- Fits in one request
- K-Exaone
- API rate not published
- Fits in one request
K-Exaone has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Gemini 2.5 Pro
- $0.0925
- Fits in one request
- K-Exaone
- API rate not published
- Fits in one request
K-Exaone has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Gemini 2.5 Pro
- $0.15
- Fits in one request
- K-Exaone
- API rate not published
- Fits in one request
- Cached-input rate unavailable
K-Exaone has no comparable published API token 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 2.5 ProK-Exaone
Context window
Maximum documented context; output-token limits may be lower.
Gemini 2.5 Pro
K-Exaone
256K
API model ID
Gemini 2.5 Pro
gemini-2.5-pro
Google Gemini API pricingK-Exaone
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 pricingK-Exaone
No comparable hosted API rate
Documented inputs
Gemini 2.5 Pro
Not sourced
K-Exaone
Not sourced
Documented outputs
Gemini 2.5 Pro
Not sourced
K-Exaone
Not sourced
Provider availability
Gemini 2.5 Pro
Not sourced
K-Exaone
Not sourced
Reasoning profile
Gemini 2.5 Pro
Non-Reasoning
K-Exaone
Reasoning
Weight access
Gemini 2.5 Pro
Proprietary
K-Exaone
Proprietary
License
Gemini 2.5 Pro
Proprietary
K-Exaone
Proprietary
Release date
Gemini 2.5 Pro
2025-03-01
K-Exaone
2025-12-31
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 2.5 Pro has the higher public point estimate, 49.07 versus 38. Their conditional score ranges overlap. These ranges do not establish rank confidence.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Gemini 2.5 Pro has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Gemini 2.5 Pro or K-Exaone?
Gemini 2.5 Pro has the higher public point estimate, 49.07 versus 38. 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 2.5 Pro or K-Exaone?
K-Exaone scores higher for coding on the public lane, 25.9 to 23.1. K-Exaone is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, Gemini 2.5 Pro or K-Exaone?
K-Exaone is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Gemini 2.5 Pro or K-Exaone?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, Gemini 2.5 Pro or K-Exaone?
Gemini 2.5 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 evidence9 rows
9 public results · 1 shared
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Last updated November 6, 2026