Coding work
Code generation, repair, and software-engineering tasks
Gemini 3.7 Flash
Gemini 3.7 Flash leads on the public coding lane, 60.3 to 55.9, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Gemini 3.7 Flash vs GPT-5.3 Codex
Updated September 27, 2026. Rank says Gemini 3.7 Flash is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
Decision reading
Gemini 3.7 Flash has the higher public score estimate, 67.66 versus 62.18, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 2
- Gemini 3.7 Flash only
- 23
- GPT-5.3 Codex only
- 8
- Like-for-like categories
- 1 / 8
Supported: Gemini 3.7 Flash and GPT-5.3 CodexHow 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.
Long documents
Prompts that approach the documented context limit
Gemini 3.7 Flash
Gemini 3.7 Flash has the larger documented context window.
Confidence: documentedChat turn cost
1K fresh input + 500 output tokens
Gemini 3.7 Flash
Gemini 3.7 Flash 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 3.7 Flash
Gemini 3.7 Flash has the lower estimated token cost for this stated workload. GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallbackRepository review cost
50K fresh input + 3K output tokens
Gemini 3.7 Flash
Gemini 3.7 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-5.3 Codex 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.
60.3Gemini 3.7 Flash55.9GPT-5.3 Codex
Like-for-like · BenchAlign v5.7
Gemini 3.7 Flash leads the like-for-like coding row, although the 90% intervals overlap.
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.
23 Gemini 3.7 Flash only
2 Shared
8 GPT-5.3 Codex 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.
LiveCodeBench (Vals)Coding
Normalized gap 1.4Gemini 3.7 Flash:88.7%GPT-5.3 Codex:87.3%
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 v5.7 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
Like-for-like- Gemini 3.7 Flash
- 60.3
- Supported · #17/135
- GPT-5.3 Codex
- 55.9
- Supported · #26/135
- Basis
- BenchAlign v5.7 lane · 6 vs 6 public rows
- Reading
- Gemini 3.7 Flash leads · intervals overlap
Agentic
Directional only- Gemini 3.7 Flash
- 58.6
- Supported · #20/105
- GPT-5.3 Codex
- 54.2
- Estimated · #31/105
- Basis
- BenchAlign v5.7 lane · 7 vs 4 public rows
- Reading
- Directional only
Knowledge
Directional only- Gemini 3.7 Flash
- 71.1
- Supported · #10/158
- GPT-5.3 Codex
- 64.3
- Estimated · #26/158
- Basis
- BenchAlign v5.7 lane · 6 vs 0 public rows
- Reading
- Directional only
Reasoning
Not comparable- Gemini 3.7 Flash
- 77.8
- Unranked · 5 rankable rows
- GPT-5.3 Codex
- 79.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 3.7 Flash
- 82.6
- #10/50
- GPT-5.3 Codex
- 75.7
- Unranked · 1 rankable row
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3.7 Flash
- Not ranked
- GPT-5.3 Codex
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 3.7 Flash
- Not ranked
- GPT-5.3 Codex
- 91.2
- #14/124
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 3.7 Flash
- Not ranked
- GPT-5.3 Codex
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemini 3.7 Flash | GPT-5.3 Codex | Basis | Reading |
|---|---|---|---|---|
| Coding | 60.3Supported · #17/135 | 55.9Supported · #26/135 | Like-for-likeBenchAlign v5.7 lane · 6 vs 6 public rows | Gemini 3.7 Flash leads · intervals overlap |
| Agentic | 58.6Supported · #20/105 | 54.2Estimated · #31/105 | Directional onlyBenchAlign v5.7 lane · 7 vs 4 public rows | Directional only |
| Knowledge | 71.1Supported · #10/158 | 64.3Estimated · #26/158 | Directional onlyBenchAlign v5.7 lane · 6 vs 0 public rows | Directional only |
| Reasoning | 77.8Unranked · 5 rankable rows | 79.3Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | 82.6#10/50 | 75.7Unranked · 1 rankable row | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 91.2#14/124 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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 3.7 Flash
- $0.00262
- Fits in one request
- GPT-5.3 Codex
- $0.00875
- Fits in one request
Gemini 3.7 Flash has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Gemini 3.7 Flash
- $0.04875
- Fits in one request
- GPT-5.3 Codex
- $0.1295
- Fits in one request
Gemini 3.7 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 3.7 Flash
- $0.0675
- Fits in one request
- GPT-5.3 Codex
- $0.525
- Fits in one request
- Cached input priced at the published list-input rate
Gemini 3.7 Flash has the lower modeled cost
GPT-5.3 Codex 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 3.7 FlashGPT-5.3 Codex
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3.7 Flash
GPT-5.3 Codex
API model ID
Gemini 3.7 Flash
gemini-3.7-flash
Google Gemini 3.7 Flash API documentationGPT-5.3 Codex
gpt-5.3-codex
OpenAI GPT-5.3 Codex model documentationCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 3.7 Flash
$0.075 per 1M cached input tokens
Google Gemini API pricingGPT-5.3 Codex
Not published
Documented inputs
Gemini 3.7 Flash
text, image, video, audio, pdf
Google Gemini 3.7 Flash API documentationGPT-5.3 Codex
text, image
OpenAI model catalogDocumented outputs
Gemini 3.7 Flash
GPT-5.3 Codex
Provider availability
Gemini 3.7 Flash
Generally Available · Gemini API, Google AI Studio, Gemini App - Spark, Gemini Enterprise App, Gemini Enterprise Agent Platform, Google Antigravity
Google DeepMind Gemini 3.7 Flash model cardGPT-5.3 Codex
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
Gemini 3.7 Flash
Reasoning
GPT-5.3 Codex
Reasoning
Weight access
Gemini 3.7 Flash
Proprietary
GPT-5.3 Codex
Proprietary
License
Gemini 3.7 Flash
Proprietary
GPT-5.3 Codex
Proprietary
Release date
Gemini 3.7 Flash
2026-08-13
GPT-5.3 Codex
2026-02-05
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 3.7 Flash has the higher public score estimate, 67.66 versus 62.18, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.04875 vs $0.1295. Cache-heavy agent loop: $0.0675 vs $0.525.
- Context tradeoff
- Gemini 3.7 Flash has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Gemini 3.7 Flash or GPT-5.3 Codex?
Gemini 3.7 Flash has the higher public score estimate, 67.66 versus 62.18, 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 3.7 Flash or GPT-5.3 Codex?
Gemini 3.7 Flash leads the public coding lane, 60.3 to 55.9, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, Gemini 3.7 Flash or GPT-5.3 Codex?
Gemini 3.7 Flash scores higher for agentic tasks on the public lane, 58.6 to 54.2. GPT-5.3 Codex 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 3.7 Flash or GPT-5.3 Codex?
For the stated presets, chat costs $0.00263 on Gemini 3.7 Flash and $0.00875 on GPT-5.3 Codex; repository review costs $0.04875 and $0.1295; the cache-heavy agent loop costs $0.0675 and $0.525. GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Gemini 3.7 Flash or GPT-5.3 Codex?
Gemini 3.7 Flash has the larger documented context window: 1M, 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 evidence33 rows
Agentic
Terminal-Bench 2.1
Gemini 3.7 Flash85.8%SourceGPT-5.3 Codex—Not directly comparable
Terminal-Bench 3.0
Gemini 3.7 Flash14.9%SourceGPT-5.3 Codex—Not directly comparable
AutomationBench
Gemini 3.7 Flash30.4%SourceGPT-5.3 Codex—Not directly comparable
OSWorld 2.0
Gemini 3.7 Flash47.9%SourceGPT-5.3 Codex—Not directly comparable
Agents' Last Exam
Gemini 3.7 Flash26.3%SourceGPT-5.3 Codex—Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.7 Flash77.5%SourceGPT-5.3 Codex—Not directly comparable
ApprenticeBench
Gemini 3.7 Flash16%SourceGPT-5.3 Codex—Not directly comparable
Terminal-Bench 2.0
Gemini 3.7 Flash—GPT-5.3 Codex77.3%SourceNot directly comparable
OSWorld-Verified
Gemini 3.7 Flash—GPT-5.3 Codex64.7%SourceNot directly comparable
Gert Labs
Gemini 3.7 Flash—GPT-5.3 Codex57.47%SourceNot directly comparable
JobBench
Gemini 3.7 Flash—GPT-5.3 Codex33.7%SourceNot directly comparable
Coding
FrontierCode 1.1 Main
Gemini 3.7 Flash43.6%SourceGPT-5.3 Codex—Not directly comparable
DeepSWE
Gemini 3.7 Flash65.3%SourceGPT-5.3 Codex—Not directly comparable
Terminal-Bench 2.1
Gemini 3.7 Flash85.8%SourceGPT-5.3 Codex—Not directly comparable
FrontierSWE v2
Gemini 3.7 Flash20.3%SourceGPT-5.3 Codex—Not directly comparable
LiveCodeBench (Vals)
Gemini 3.7 Flash88.7%SourceGPT-5.3 Codex87.3%SourceGemini 3.7 Flash leads this result
SWE-bench (Vals)
Gemini 3.7 Flash80.8%SourceGPT-5.3 Codex78.0%SourceGemini 3.7 Flash leads this result
SWE-bench Verified
Gemini 3.7 Flash—GPT-5.3 Codex85%SourceNot directly comparable
SWE-bench Pro
Gemini 3.7 Flash—GPT-5.3 Codex56.8%SourceNot directly comparable
SWE-Rebench
Gemini 3.7 Flash—GPT-5.3 Codex58.2%SourceNot directly comparable
Vibe Code Bench
Gemini 3.7 Flash—GPT-5.3 Codex61.77%SourceNot directly comparable
Reasoning
Multimodal
Knowledge
HLE-Verified
Gemini 3.7 Flash53.6%SourceGPT-5.3 Codex—Not directly comparable
LABBench2
Gemini 3.7 Flash82.1%SourceGPT-5.3 Codex—Not directly comparable
BioMysteryBench (human-solvable)
Gemini 3.7 Flash87.1%SourceGPT-5.3 Codex—Not directly comparable
BioMysteryBench (human-difficult)
Gemini 3.7 Flash43.5%SourceGPT-5.3 Codex—Not directly comparable
GPQA Diamond (Vals)
Gemini 3.7 Flash93.9%SourceGPT-5.3 Codex—Not directly comparable
MMLU-Pro (Vals)
Gemini 3.7 Flash90.1%SourceGPT-5.3 Codex—Not directly comparable
33 public results · 2 shared
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Last updated September 27, 2026