Long documents
Prompts that approach the documented context limit
Gemini 3 Pro
Gemini 3 Pro has the larger documented context window.
Confidence: documented
Gemini 3 Pro vs GPT-5.2
Updated October 17, 2026. Rank says Gemini 3 Pro is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Share or export
Decision reading
Gemini 3 Pro has the higher public score estimate, 61.18 versus 59.86, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 23 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 23
- Gemini 3 Pro only
- 4
- GPT-5.2 only
- 8
- Like-for-like categories
- 1 / 8
Supported: Gemini 3 Pro and GPT-5.2How 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
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-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-5.2
GPT-5.2 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. GPT-5.2 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
GPT-5.2
GPT-5.2 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
Gemini 3 Pro 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
Gemini 3 Pro is not ranked on the public lane for agentic, so no winner is named for agentic.
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.
43.0Gemini 3 Pro36.8GPT-5.2
Directional only · BenchAlign v5.8
Gemini 3 Pro 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.
4 Gemini 3 Pro only
23 Shared
8 GPT-5.2 only
3 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.
ARC-AGI-2Reasoning
Normalized gap 21.8Gemini 3 Pro:31.1%GPT-5.2:52.9%MMMU-ProMultimodal
Normalized gap 1.5Gemini 3 Pro:81%GPT-5.2:79.5%CharXivMultimodal
Normalized gap 0.7Gemini 3 Pro:81.4%GPT-5.2:82.1%
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 Pro
- 80.0
- #18/54
- GPT-5.2
- 75.9
- #22/54
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Gemini 3 Pro leads
Coding
Directional only- Gemini 3 Pro
- 43.0
- Estimated · #59/150
- GPT-5.2
- 36.8
- Supported · #73/150
- Basis
- BenchAlign v5.8 lane · 1 vs 3 public rows
- Reading
- Directional only
Knowledge
Directional only- Gemini 3 Pro
- 60.8
- Estimated · #47/184
- GPT-5.2
- 59.7
- Supported · #50/184
- Basis
- BenchAlign v5.8 lane · 0 vs 1 public rows
- Reading
- Directional only
Instruction following
Directional only- Gemini 3 Pro
- 86.1
- #45/148
- GPT-5.2
- 92.6
- #19/148
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Agentic
Not comparable- Gemini 3 Pro
- Not ranked
- GPT-5.2
- 44.4
- Supported · #58/128
- Basis
- BenchAlign v5.8 lane · 2 vs 4 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemini 3 Pro
- 50.1
- Unranked · 4 rankable rows
- GPT-5.2
- 64.3
- Unranked · 4 rankable rows
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3 Pro
- Not ranked
- GPT-5.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 3 Pro
- Not ranked
- GPT-5.2
- 71.1
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
| Category | Gemini 3 Pro | GPT-5.2 | Basis | Reading |
|---|---|---|---|---|
| Multimodal | 80.0#18/54 | 75.9#22/54 | Like-for-likeProvisional lane · 2 vs 2 weighted rows | Gemini 3 Pro leads |
| Coding | 43.0Estimated · #59/150 | 36.8Supported · #73/150 | Directional onlyBenchAlign v5.8 lane · 1 vs 3 public rows | Directional only |
| Knowledge | 60.8Estimated · #47/184 | 59.7Supported · #50/184 | Directional onlyBenchAlign v5.8 lane · 0 vs 1 public rows | Directional only |
| Instruction following | 86.1#45/148 | 92.6#19/148 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Agentic | Not ranked | 44.4Supported · #58/128 | Not comparableBenchAlign v5.8 lane · 2 vs 4 public rows | Not comparable |
| Reasoning | 50.1Unranked · 4 rankable rows | 64.3Unranked · 4 rankable rows | Not comparableProvisional lane · 1 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 71.1Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 2 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 Pro
- $0.008
- Fits in one request
- GPT-5.2
- $0.00875
- 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
- GPT-5.2
- $0.1295
- Fits in one request
GPT-5.2 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
- GPT-5.2
- $0.525
- Fits in one request
- Cached input priced at the published list-input rate
GPT-5.2 has the lower modeled cost
Gemini 3 Pro has no published cached-input rate, so cached tokens use its listed input rate. GPT-5.2 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 ProGPT-5.2
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3 Pro
2M
GPT-5.2
400K
API model ID
Gemini 3 Pro
Not sourced
GPT-5.2
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
GPT-5.2
Not published
Documented inputs
Gemini 3 Pro
Not sourced
GPT-5.2
Not sourced
Documented outputs
Gemini 3 Pro
Not sourced
GPT-5.2
Not sourced
Provider availability
Gemini 3 Pro
Not sourced
GPT-5.2
Not sourced
Reasoning profile
Gemini 3 Pro
Non-Reasoning
GPT-5.2
Reasoning
Weight access
Gemini 3 Pro
Proprietary
GPT-5.2
Proprietary
License
Gemini 3 Pro
Proprietary
GPT-5.2
Proprietary
Release date
Gemini 3 Pro
2025-11-18
GPT-5.2
2025-12-11
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 estimate, 61.18 versus 59.86, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.136 vs $0.1295. Cache-heavy agent loop: $0.56 vs $0.525.
- Context tradeoff
- Gemini 3 Pro has the larger documented window (2M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Gemini 3 Pro or GPT-5.2?
Gemini 3 Pro has the higher public score estimate, 61.18 versus 59.86, 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 GPT-5.2?
Gemini 3 Pro scores higher for coding on the public lane, 43 to 36.8. Gemini 3 Pro 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 3 Pro or GPT-5.2?
Gemini 3 Pro is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Gemini 3 Pro or GPT-5.2?
For the stated presets, chat costs $0.008 on Gemini 3 Pro and $0.00875 on GPT-5.2; repository review costs $0.136 and $0.1295; the cache-heavy agent loop costs $0.56 and $0.525. Gemini 3 Pro has no published cached-input rate, so cached tokens use its listed input rate. GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Gemini 3 Pro or GPT-5.2?
Gemini 3 Pro has the larger documented context window: 2M, compared with 400K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence35 rows
Agentic
Gert Labs
Shared sourceGemini 3 Pro63.23%GPT-5.246.54%Gemini 3 Pro leads this result
JobBench
Shared sourceGemini 3 Pro11.4%GPT-5.234.3%GPT-5.2 leads this result
BrowseComp
Gemini 3 Pro—GPT-5.265.8%SourceNot directly comparable
OSWorld-Verified
Gemini 3 Pro—GPT-5.247.3%SourceNot directly comparable
Coding
Vibe Code Bench
Shared sourceGemini 3 Pro14.30%GPT-5.253.50%GPT-5.2 leads this result
SWE-bench Verified
Gemini 3 Pro—GPT-5.280%SourceNot directly comparable
SWE-bench Pro
Gemini 3 Pro—GPT-5.255.6%SourceNot directly comparable
Reasoning
ARC-AGI-2
Gemini 3 Pro31.1%SourceGPT-5.252.9%SourceGPT-5.2 leads this result
ARC-AGI-1
Shared sourceGemini 3 Pro75.00%GPT-5.286.17%GPT-5.2 leads this result
Multimodal
MMMU
Shared sourceGemini 3 Pro87.2%GPT-5.286.7%Gemini 3 Pro leads this result
MMMU-Pro
Gemini 3 Pro81%SourceGPT-5.279.5%SourceGemini 3 Pro leads this result
RealWorldQA
Shared sourceGemini 3 Pro83.3%GPT-5.283.3%Tie
OmniDocBench 1.5
Shared sourceGemini 3 Pro88.5%GPT-5.285.7%Gemini 3 Pro leads this result
Video-MME (with subtitle)
Shared sourceGemini 3 Pro88.4%GPT-5.286.0%Gemini 3 Pro leads this result
Video-MME (w/o subtitle)
Shared sourceGemini 3 Pro87.7%GPT-5.285.8%Gemini 3 Pro leads this result
MathVision
Shared sourceGemini 3 Pro86.6%GPT-5.283.0%Gemini 3 Pro leads this result
We-Math
Shared sourceGemini 3 Pro86.9%GPT-5.279.0%Gemini 3 Pro leads this result
DynaMath
Shared sourceGemini 3 Pro85.1%GPT-5.286.8%GPT-5.2 leads this result
SimpleVQA
Shared sourceGemini 3 Pro73.2%GPT-5.255.8%Gemini 3 Pro leads this result
MMLongBench-Doc
Gemini 3 Pro60.5%SourceGPT-5.2—Not directly comparable
CC-OCR
Shared sourceGemini 3 Pro79.0%GPT-5.270.3%Gemini 3 Pro leads this result
AI2D_TEST
Shared sourceGemini 3 Pro94.1%GPT-5.292.2%Gemini 3 Pro leads this result
CountBench
Shared sourceGemini 3 Pro97.3%GPT-5.291.9%Gemini 3 Pro leads this result
RefCOCO (avg)
Gemini 3 Pro84.1%SourceGPT-5.2—Not directly comparable
ODINW13
Gemini 3 Pro46.3%SourceGPT-5.2—Not directly comparable
ERQA
Shared sourceGemini 3 Pro70.5%GPT-5.259.8%Gemini 3 Pro leads this result
VideoMMMU
Gemini 3 Pro87.6%SourceGPT-5.285.9%SourceGemini 3 Pro leads this result
MLVU (M-Avg)
Shared sourceGemini 3 Pro83.0%GPT-5.285.6%GPT-5.2 leads this result
ScreenSpot Pro
Gemini 3 Pro72.7%SourceGPT-5.2—Not directly comparable
CharXiv
Shared sourceGemini 3 Pro81.4%GPT-5.282.1%GPT-5.2 leads this result
- Gemini 3 Pro88.0%GPT-5.275.9%
Gemini 3 Pro leads this result
Knowledge
GPQA
Gemini 3 Pro—GPT-5.292.4%SourceNot directly comparable
Math
35 public results · 23 shared
Watch Gemini 3 Pro vs GPT-5.2
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 5,600+ readers.
Last updated October 17, 2026