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BenchLM
Model A
Gemini 3.5 Flash

Google

64.7/100

Estimated · Public rank #39

90% interval 54.1–75.2

Gemini 3.5 Flash vs GPT-5.3 Codex

Updated August 18, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
GPT-5.3 Codex

OpenAI

65.9/100

Supported · Public rank #32

90% interval 62.3–69.5

Decision reading

GPT-5.3 Codex has the higher public score estimate, 65.93 versus 64.67, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

5 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.

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Gemini 3.5 Flash

    Gemini 3.5 Flash leads on the same 2 weighted benchmark rows.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    Gemini 3.5 Flash

    Gemini 3.5 Flash has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Gemini 3.5 Flash

    Gemini 3.5 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 3.5 Flash

    Gemini 3.5 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-fallback

  • Repository review cost

    50K fresh input + 3K output tokens

    Gemini 3.5 Flash

    Gemini 3.5 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

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    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.

Shared results
5
Gemini 3.5 Flash only
18
GPT-5.3 Codex only
3
Like-for-like categories
1 / 8

1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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

Like-for-like
Gemini 3.5 Flash
77.2
GPT-5.3 Codex
71.4
Weighted basis
2 vs 2 rows
Reading
Gemini 3.5 Flash leads

Coding

Directional only
Gemini 3.5 Flash
55.1
GPT-5.3 Codex
67.2
Weighted basis
1 vs 3 rows
Reading
Directional only

Reasoning

Not comparable
Gemini 3.5 Flash
74.7
GPT-5.3 Codex
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Gemini 3.5 Flash
40.2
GPT-5.3 Codex
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Math

Not comparable
Gemini 3.5 Flash
32.9
GPT-5.3 Codex
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 3.5 Flash
Not measured
GPT-5.3 Codex
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 3.5 Flash
83.8
GPT-5.3 Codex
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 3.5 Flash
Not measured
GPT-5.3 Codex
Not measured
Weighted basis
0 vs 0 rows
Reading
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.

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 3.5 Flash
$0.006
Fits in one request
GPT-5.3 Codex
$0.00875
Fits in one request

Gemini 3.5 Flash has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Gemini 3.5 Flash
$0.102
Fits in one request
GPT-5.3 Codex
$0.1295
Fits in one request

Gemini 3.5 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 3.5 Flash
$0.15
Fits in one request
GPT-5.3 Codex
$0.525
Fits in one request
Cached input priced at the published list-input rate

Gemini 3.5 Flash has the lower modeled cost

GPT-5.3 Codex 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.

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Gemini 3.5 Flash

$0.15 per 1M cached input tokens

Google Gemini API pricing

GPT-5.3 Codex

Not published

Provider availability

Gemini 3.5 Flash

Not sourced

GPT-5.3 Codex

Generally Available · OpenAI Responses API

OpenAI model catalog

Reasoning profile

Gemini 3.5 Flash

Reasoning

GPT-5.3 Codex

Reasoning

Weight access

Gemini 3.5 Flash

Proprietary

GPT-5.3 Codex

Proprietary

License

Gemini 3.5 Flash

Proprietary

GPT-5.3 Codex

Proprietary

Release date

Gemini 3.5 Flash

2026-05-19

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
GPT-5.3 Codex has the higher public score estimate, 65.93 versus 64.67, but the 90% score intervals overlap.
Workload cost
Repository review: $0.102 vs $0.1295. Cache-heavy agent loop: $0.15 vs $0.525.
Context tradeoff
Gemini 3.5 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 evidence26 rows

Agentic

  • Terminal-Bench 2.0

    Gemini 3.5 Flash76.2%
    Source
    GPT-5.3 Codex77.3%
    Source

    GPT-5.3 Codex leads this result

  • MCP Atlas

    Gemini 3.5 Flash83.6%
    Source
    GPT-5.3 Codex—

    Not directly comparable

  • Toolathlon

    Gemini 3.5 Flash56.5%
    Source
    GPT-5.3 Codex—

    Not directly comparable

  • OSWorld-Verified

    Gemini 3.5 Flash78.4%
    Source
    GPT-5.3 Codex64.7%
    Source

    Gemini 3.5 Flash leads this result

  • Finance Agent v2

    Gemini 3.5 Flash57.9%
    Source
    GPT-5.3 Codex—

    Not directly comparable

  • Gert Labs

    Shared source
    Gemini 3.5 Flash61.85%
    GPT-5.3 Codex57.47%

    Gemini 3.5 Flash leads this result

  • ResearchClawBench

    Gemini 3.5 Flash18.0%
    Source
    GPT-5.3 Codex—

    Not directly comparable

  • JobBench

    Gemini 3.5 Flash—
    GPT-5.3 Codex33.7%
    Source

    Not directly comparable

Coding

  • Terminal-Bench 2.0

    Gemini 3.5 Flash76.2%
    Source
    GPT-5.3 Codex—

    Not directly comparable

  • SWE-bench Pro

    Gemini 3.5 Flash55.1%
    Source
    GPT-5.3 Codex56.8%
    Source

    GPT-5.3 Codex leads this result

  • Vibe Code Bench

    Shared source
    Gemini 3.5 Flash48.68%
    GPT-5.3 Codex61.77%

    GPT-5.3 Codex leads this result

  • cursorBench31

    Gemini 3.5 Flash49.8%
    Source
    GPT-5.3 Codex—

    Not directly comparable

  • cursorBench32

    Gemini 3.5 Flash48.8%
    Source
    GPT-5.3 Codex—

    Not directly comparable

  • EEBench

    Gemini 3.5 Flash34.3%
    Source
    GPT-5.3 Codex—

    Not directly comparable

  • SWE-bench Verified

    Gemini 3.5 Flash—
    GPT-5.3 Codex85%
    Source

    Not directly comparable

  • SWE-Rebench

    Gemini 3.5 Flash—
    GPT-5.3 Codex58.2%
    Source

    Not directly comparable

Reasoning

  • MRCRv2

    Gemini 3.5 Flash77.3%
    Source
    GPT-5.3 Codex—

    Not directly comparable

  • MRCR 1M

    Gemini 3.5 Flash26.6%
    Source
    GPT-5.3 Codex—

    Not directly comparable

  • ARC-AGI-2

    Gemini 3.5 Flash72.1%
    Source
    GPT-5.3 Codex—

    Not directly comparable

Knowledge

  • GPQA-D

    Gemini 3.5 Flash92.7%
    Source
    GPT-5.3 Codex—

    Not directly comparable

  • HLE

    Gemini 3.5 Flash40.2%
    Source
    GPT-5.3 Codex—

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Gemini 3.5 Flash38.966%
    Source
    GPT-5.3 Codex—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Gemini 3.5 Flash14.583%
    Source
    GPT-5.3 Codex—

    Not directly comparable

Multimodal

  • CharXiv

    Gemini 3.5 Flash84.2%
    Source
    GPT-5.3 Codex—

    Not directly comparable

  • MMMU-Pro

    Gemini 3.5 Flash83.6%
    Source
    GPT-5.3 Codex—

    Not directly comparable

  • Blueprint-Bench 2

    Gemini 3.5 Flash33.6%
    Source
    GPT-5.3 Codex—

    Not directly comparable

Frequently asked questions

Which is better, Gemini 3.5 Flash or GPT-5.3 Codex?

GPT-5.3 Codex has the higher public score estimate, 65.93 versus 64.67, 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 3.5 Flash or GPT-5.3 Codex?

The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Gemini 3.5 Flash or GPT-5.3 Codex?

Gemini 3.5 Flash leads the like-for-like agentic tasks comparison across 2 shared weighted benchmark rows.

Which costs less, Gemini 3.5 Flash or GPT-5.3 Codex?

For the stated presets, chat costs $0.006 on Gemini 3.5 Flash and $0.00875 on GPT-5.3 Codex; repository review costs $0.102 and $0.1295; the cache-heavy agent loop costs $0.15 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 3.5 Flash or GPT-5.3 Codex?

Gemini 3.5 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 18, 2026

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