Math
Like-for-like- Gemini 2.5 Flash
- 4.7
- GPT-5.2
- 35.2
- Weighted basis
- 2 vs 2 rows
- Reading
- GPT-5.2 leads
Gemini 2.5 Flash vs GPT-5.2
Updated August 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GPT-5.2 has the higher public score estimate, 57.95 versus 47.29, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
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 2.5 Flash
Gemini 2.5 Flash has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Gemini 2.5 Flash
Gemini 2.5 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Gemini 2.5 Flash
Gemini 2.5 Flash has the lower estimated token cost for this stated workload. 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
Gemini 2.5 Flash
Gemini 2.5 Flash 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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 Shared
13 GPT-5.2 only
- Shared results
- 2
- Gemini 2.5 Flash only
- 0
- GPT-5.2 only
- 13
- 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 2.5 Flash
- Not measured
- GPT-5.2
- 55.7
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Coding
Not comparable- Gemini 2.5 Flash
- Not measured
- GPT-5.2
- 70.6
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemini 2.5 Flash
- Not measured
- GPT-5.2
- 52.9
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Knowledge
Not comparable- Gemini 2.5 Flash
- Not measured
- GPT-5.2
- 92.4
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 2.5 Flash
- Not measured
- GPT-5.2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 2.5 Flash
- Not measured
- GPT-5.2
- 80.4
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 2.5 Flash
- Not measured
- GPT-5.2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Gemini 2.5 Flash | GPT-5.2 | Weighted basis | Reading |
|---|---|---|---|---|
| Math | 4.7 | 35.2 | Like-for-like2 vs 2 rows | GPT-5.2 leads |
| Agentic | Not measured | 55.7 | Not comparable0 vs 2 rows | Not comparable |
| Coding | Not measured | 70.6 | Not comparable0 vs 2 rows | Not comparable |
| Reasoning | Not measured | 52.9 | Not comparable0 vs 1 rows | Not comparable |
| Knowledge | Not measured | 92.4 | Not comparable0 vs 1 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 80.4 | Not comparable0 vs 2 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 (Tiers 1-3)
Math
FrontierMath v2 (Tier 4)
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 2.5 Flash
- $0.00155
- Fits in one request
- GPT-5.2
- $0.00875
- Fits in one request
Gemini 2.5 Flash has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Gemini 2.5 Flash
- $0.0225
- Fits in one request
- GPT-5.2
- $0.1295
- Fits in one request
Gemini 2.5 Flash 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 Flash
- $0.037
- Fits in one request
- GPT-5.2
- $0.525
- Fits in one request
- Cached input priced at the published list-input rate
Gemini 2.5 Flash has the lower modeled cost
GPT-5.2 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 FlashGPT-5.2
Context window
Maximum documented context; output-token limits may be lower.
Gemini 2.5 Flash
GPT-5.2
400K
API model ID
Gemini 2.5 Flash
gemini-2.5-flash
Google Gemini API pricingGPT-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 2.5 Flash
$0.03 per 1M cached input tokens
Google Gemini API pricingGPT-5.2
Not published
Documented inputs
Gemini 2.5 Flash
Not sourced
GPT-5.2
Not sourced
Documented outputs
Gemini 2.5 Flash
Not sourced
GPT-5.2
Not sourced
Provider availability
Gemini 2.5 Flash
Not sourced
GPT-5.2
Not sourced
Reasoning profile
Gemini 2.5 Flash
Non-Reasoning
GPT-5.2
Reasoning
Weight access
Gemini 2.5 Flash
Proprietary
GPT-5.2
Proprietary
License
Gemini 2.5 Flash
Proprietary
GPT-5.2
Proprietary
Release date
Gemini 2.5 Flash
2025-06-17
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
- GPT-5.2 has the higher public score estimate, 57.95 versus 47.29, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.0225 vs $0.1295. Cache-heavy agent loop: $0.037 vs $0.525.
- Context tradeoff
- Gemini 2.5 Flash 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 evidence15 rows
Agentic
Coding
Reasoning
ARC-AGI-2
Gemini 2.5 Flash—GPT-5.252.9%SourceNot directly comparable
Knowledge
GPQA
Gemini 2.5 Flash—GPT-5.292.4%SourceNot directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceGemini 2.5 Flash4.844%GPT-5.240.700%GPT-5.2 leads this result
FrontierMath v2 (Tier 4)
Shared sourceGemini 2.5 Flash4.167%GPT-5.218.800%GPT-5.2 leads this result
Multimodal
Frequently asked questions
Which is better, Gemini 2.5 Flash or GPT-5.2?
GPT-5.2 has the higher public score estimate, 57.95 versus 47.29, 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 Flash or GPT-5.2?
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 2.5 Flash or GPT-5.2?
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 Flash or GPT-5.2?
For the stated presets, chat costs $0.00155 on Gemini 2.5 Flash and $0.00875 on GPT-5.2; repository review costs $0.0225 and $0.1295; the cache-heavy agent loop costs $0.037 and $0.525. GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Gemini 2.5 Flash or GPT-5.2?
Gemini 2.5 Flash has the larger documented context window: 1M, compared with 400K.
Related comparisons
Last updated August 14, 2026
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