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Gemini 4 Argon vs GPT-5.3 Codex

Gemini 4 Argon leads the LLM Stats Score 55.1 to 36.3. Gemini 4 Argon is 1.2x cheaper per token.

Google · OpenAI · Updated for 2026

Which is better?

Gemini 4 Argon leads the overall LLM Stats Score 55.1 to 36.3, ranking #4 overall.

On price, Gemini 4 Argon is roughly 1.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

Gemini 4 Argon also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose Gemini 4 Argon

  • overall performance matters — it scores 55.1 and ranks #4 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • cost matters — it's about 1.2x cheaper per token
  • you process long inputs — it offers a 1,048,576 token context window
  • you want the most recent training data — it shipped Sep 2026

Choose GPT-5.3 Codex

  • you want predictable pricing at $1.75/M input and $14.00/M output

At a glance

The differences that matter most.

Gemini 4 Argon
Google
GPT-5.3 Codex
OpenAI
Core performance indexes
LLM Stats Score
55.1
#4
36.3
#96
Reasoning
53.2
#6
37.0
#87
Coding
42.5
#7
29.5
#64
Agents
41.2
#4
22.7
#76
Cost, coverage & limits
Benchmark wins
—
—
Input price
$2.00 / M
$1.75 / M
Output price
$10.00 / M
$14.00 / M
Context window
1,048,576
400,000

Individual benchmarks

19 reported for Gemini 4 Argon · 6 for GPT-5.3 Codex

No common benchmarks found

Gemini 4 Argon and GPT-5.3 Codexdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

Gemini 4 Argon costs less

For input processing, Gemini 4 Argon ($2.00/1M tokens) is 1.1x more expensive than GPT-5.3 Codex ($1.75/1M tokens).

For output processing, Gemini 4 Argon ($10.00/1M tokens) is 1.4x cheaper than GPT-5.3 Codex ($14.00/1M tokens).

In conclusion, GPT-5.3 Codex is more expensive than Gemini 4 Argon.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Thu Oct 22 2026 • llm-stats.com
Google
Gemini 4 Argon
Input tokens$2.00
Output tokens$10.00
Best providerGoogle
OpenAI
GPT-5.3 Codex
Input tokens$1.75
Output tokens$14.00
Best providerOpenAI
Notice missing or incorrect data?

Context Window

Maximum input and output token capacity

Gemini 4 Argon accepts 1,048,576 input tokens compared to GPT-5.3 Codex's 400,000 tokens. Gemini 4 Argon can generate longer responses up to 1,048,576 tokens, while GPT-5.3 Codex is limited to 128,000 tokens.

Google
Gemini 4 Argon
Input1,048,576 tokens
Output1,048,576 tokens
OpenAI
GPT-5.3 Codex
Input400,000 tokens
Output128,000 tokens
Thu Oct 22 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both Gemini 4 Argon and GPT-5.3 Codex support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

Gemini 4 Argon

Text
Images
Audio
Video

GPT-5.3 Codex

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under proprietary licenses.

Both models have usage restrictions defined by their respective organizations.

Gemini 4 Argon

Proprietary

Closed source

GPT-5.3 Codex

Proprietary

Closed source

Release Timeline

When each model was launched

Gemini 4 Argon was released on 2026-09-30, while GPT-5.3 Codex was released on 2026-02-05.

Gemini 4 Argon is 8 months newer than GPT-5.3 Codex.

Gemini 4 Argon

Sep 30, 2026

3 weeks ago

7mo newer
GPT-5.3 Codex

Feb 5, 2026

8 months ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

Gemini 4 Argon is available from Google. GPT-5.3 Codex is available from OpenAI.

Gemini 4 Argon

google logo
Google
Input Price:Input: $2.00/1MOutput Price:Output: $10.00/1M

GPT-5.3 Codex

openai logo
OpenAI
Input Price:Input: $1.75/1MOutput Price:Output: $14.00/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against Gemini 4 Argon and GPT-5.3 Codex side-by-side, then vote on the output you prefer.

Gemini 4 Argon
✓ Preferred
GPT-5.3 Codex
Open in Playground

FAQ

Common questions about Gemini 4 Argon vs GPT-5.3 Codex.

Which is better, Gemini 4 Argon or GPT-5.3 Codex?

Gemini 4 Argon leads the LLM Stats Score 55.1 to 36.3. Gemini 4 Argon is made by Google and GPT-5.3 Codex is made by OpenAI. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does Gemini 4 Argon compare to GPT-5.3 Codex in benchmarks?

Gemini 4 Argon scores Graphwalks BFS <128k: 99.7%, Vibe Code Bench: 91.9%, LVBench: 91.7%, LABBench2: 88.8%, Graphwalks BFS >128k: 84.2%. GPT-5.3 Codex scores SWE-Lancer (IC-Diamond subset): 81.4%, Cybersecurity CTFs: 77.6%, Terminal-Bench 2.0: 77.3%, LiveBench: 72.8%, OSWorld-Verified: 64.7%.

Is Gemini 4 Argon cheaper than GPT-5.3 Codex?

GPT-5.3 Codex is 1.1x cheaper for input tokens. Gemini 4 Argon costs $2.00/M input and $10.00/M output via google. GPT-5.3 Codex costs $1.75/M input and $14.00/M output via openai.

What are the context window sizes for Gemini 4 Argon and GPT-5.3 Codex?

Gemini 4 Argon supports 1.0M tokens and GPT-5.3 Codex supports 400K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Gemini 4 Argon and GPT-5.3 Codex?

Key differences include LLM Stats Score (55.1 vs 36.3), context window (1.0M vs 400K), input pricing ($2.00 vs $1.75/M). See the full comparison above for benchmark-by-benchmark results.

Who makes Gemini 4 Argon and GPT-5.3 Codex?

Gemini 4 Argon is developed by Google and GPT-5.3 Codex is developed by OpenAI.

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