Model A

Google

—

Evidence status unavailable

90% interval unavailable

Gemini 3.7 Flash vs GPT-5.3 Codex

Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

GPT-5.3 Codex

OpenAI

65.8/100

Supported · Public rank #31

90% interval 62.2–69.3

Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 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 3.7 Flash
Gemini 3.7 Flash has the larger documented context window.
Confidence: documented
Chat 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
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. Gemini 3.7 Flash has no published cached-input rate, so cached tokens use its listed input rate. GPT-5.3 Codex 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 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
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.

15 Gemini 3.7 Flash only

8 GPT-5.3 Codex only

Shared results: 0
Gemini 3.7 Flash only: 15
GPT-5.3 Codex only: 8
Like-for-like categories: 0 / 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 3.7 Flash: Not measured
GPT-5.3 Codex: 71.4
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Coding

Not comparable

Gemini 3.7 Flash: Not measured
GPT-5.3 Codex: 67.2
Weighted basis: 0 vs 3 rows
Reading: Not comparable

Reasoning

Not comparable

Gemini 3.7 Flash: Not measured
GPT-5.3 Codex: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Knowledge

Not comparable

Gemini 3.7 Flash: Not measured
GPT-5.3 Codex: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Math

Not comparable

Gemini 3.7 Flash: Not measured
GPT-5.3 Codex: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Multilingual

Not comparable

Gemini 3.7 Flash: Not measured
GPT-5.3 Codex: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Multimodal

Not comparable

Gemini 3.7 Flash: 88.7
GPT-5.3 Codex: Not measured
Weighted basis: 1 vs 0 rows
Reading: Not comparable

Instruction following

Not comparable

Gemini 3.7 Flash: Not measured
GPT-5.3 Codex: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for Gemini 3.7 Flash and GPT-5.3 Codex
Category	Gemini 3.7 Flash	GPT-5.3 Codex	Weighted basis	Reading
Agentic	Not measured	71.4	Not comparable0 vs 2 rows	Not comparable
Coding	Not measured	67.2	Not comparable0 vs 3 rows	Not comparable
Reasoning	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Knowledge	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Math	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Multilingual	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Multimodal	88.7	Not measured	Not comparable1 vs 0 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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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: $0.2025; Fits in one request; Cached input priced at the published list-input rate
GPT-5.3 Codex: $0.525; Fits in one request; Cached input priced at the published list-input rate

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: The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost: Repository review: $0.04875 vs $0.1295. Cache-heavy agent loop: $0.2025 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.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence23 rows

Agentic

Terminal-Bench 2.1
Gemini 3.7 Flash85.8%
Source
GPT-5.3 Codex—
Not directly comparable
Terminal-Bench 3.0
Gemini 3.7 Flash14.9%
Source
GPT-5.3 Codex—
Not directly comparable
AutomationBench
Gemini 3.7 Flash30.4%
Source
GPT-5.3 Codex—
Not directly comparable
OSWorld 2.0
Gemini 3.7 Flash47.9%
Source
GPT-5.3 Codex—
Not directly comparable
Agents' Last Exam
Gemini 3.7 Flash26.3%
Source
GPT-5.3 Codex—
Not directly comparable
Terminal-Bench 2.0
Gemini 3.7 Flash—
GPT-5.3 Codex77.3%
Source
Not directly comparable
OSWorld-Verified
Gemini 3.7 Flash—
GPT-5.3 Codex64.7%
Source
Not directly comparable
Gert Labs
Gemini 3.7 Flash—
GPT-5.3 Codex57.47%
Source
Not directly comparable
JobBench
Gemini 3.7 Flash—
GPT-5.3 Codex33.7%
Source
Not directly comparable

Coding

FrontierCode 1.1 Main
Gemini 3.7 Flash43.6%
Source
GPT-5.3 Codex—
Not directly comparable
deepSwe
Gemini 3.7 Flash65.3%
Source
GPT-5.3 Codex—
Not directly comparable
Terminal-Bench 2.1
Gemini 3.7 Flash85.8%
Source
GPT-5.3 Codex—
Not directly comparable
SWE-bench Verified
Gemini 3.7 Flash—
GPT-5.3 Codex85%
Source
Not directly comparable
SWE-bench Pro
Gemini 3.7 Flash—
GPT-5.3 Codex56.8%
Source
Not directly comparable
SWE-Rebench
Gemini 3.7 Flash—
GPT-5.3 Codex58.2%
Source
Not directly comparable
Vibe Code Bench
Gemini 3.7 Flash—
GPT-5.3 Codex61.77%
Source
Not directly comparable

Reasoning

GDM-MRCR v2 128K average
Gemini 3.7 Flash97%
Source
GPT-5.3 Codex—
Not directly comparable

Knowledge

BioMysteryBench (human-solvable)
Gemini 3.7 Flash87.1%
Source
GPT-5.3 Codex—
Not directly comparable
BioMysteryBench (human-difficult)
Gemini 3.7 Flash43.5%
Source
GPT-5.3 Codex—
Not directly comparable

Multimodal

GDP.pdf (no tools)
Gemini 3.7 Flash34.0%
Source
GPT-5.3 Codex—
Not directly comparable
CharXiv w/o tools
Gemini 3.7 Flash84.5%
Source
GPT-5.3 Codex—
Not directly comparable
CharXiv
Gemini 3.7 Flash88.7%
Source
GPT-5.3 Codex—
Not directly comparable
LVBench
Gemini 3.7 Flash85.4%
Source
GPT-5.3 Codex—
Not directly comparable

Frequently asked questions

Which is better, Gemini 3.7 Flash or GPT-5.3 Codex?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Gemini 3.7 Flash or GPT-5.3 Codex?

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 3.7 Flash or GPT-5.3 Codex?

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 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 $0.2025 and $0.525. Gemini 3.7 Flash has no published cached-input rate, so cached tokens use its listed input rate. 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.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 13, 2026

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Gemini 3.7 Flash vs GPT-5.3 Codex

Which one for your work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Coding work

Agentic work

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Reasoning

Knowledge

Math

Multilingual

Multimodal

Instruction following

Shape of the matched evidence

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Multimodal

Frequently asked questions

Which is better, Gemini 3.7 Flash or GPT-5.3 Codex?

Which is better for coding, Gemini 3.7 Flash or GPT-5.3 Codex?

Which is better for agentic tasks, Gemini 3.7 Flash or GPT-5.3 Codex?

Which costs less, Gemini 3.7 Flash or GPT-5.3 Codex?

Which has the larger context window, Gemini 3.7 Flash or GPT-5.3 Codex?

Related comparisons

Watch Gemini 3.7 Flash vs GPT-5.3 Codex