Standard Compute
Model comparison 2026

DeepSeek R1 (0528) vs GPT-5.3-Codex

DeepSeek R1 (0528)
DeepSeek · Balanced · Open weights
$0.99/1M · 164K ctx
GPT-5.3-Codex
OpenAI · Balanced · Proprietary
$5.42/1M · 400K ctx

Community vote

Pick a winner in each dimension. You can change your vote any time.

Output Quality

Correctness and depth of what it produces

Agentic Ability

Tool calls, instruction-following, and multi-step tasks

Speed

Tokens per second and time-to-first-token

Value for Money

How much capability you get per dollar

Reliability

Consistent results: fewer refusals, loops and format breaks

Specs and pricing side by side

Spec
DeepSeek R1
GPT-5.3-Codex
Maker
DeepSeek
OpenAI
Blended price / 1M
$0.99
$5.42
Input / output
$0.50 in · $2.15 out / 1M tokens
$1.75 in · $14 out / 1M tokens
Context window
164K
400K
Open weights
Yes
No
Tool use
Yes
Yes
Reasoning
Yes
Yes
Output Quality
4.0
6.8
Agentic Ability
4.4
5.1
Speed
5.1
8.0
Value for Money
4.7
5.0
Reliability
8.4
8.4

Pricing and capabilities synced from the OpenRouter catalogue. Scores are editorial (0–10).

Verdict: DeepSeek R1 or GPT-5.3-Codex?

Updated 2026-10-17

Choose DeepSeek R1 (0528) if you want research and comparisons that need the original R1 reasoning model. Choose GPT-5.3-Codex if you want existing Codex setups on an API key that still pin GPT-5.3-Codex.

In our editorial scoring, GPT-5.3-Codex leads in 4 of five dimensions (output quality, agentic ability, speed and value for money), while DeepSeek R1 (0528) leads in 0. On price, DeepSeek R1 (0528) runs about $0.99 per 1M tokens (blended) and is open-weight; GPT-5.3-Codex is about $5.42 and proprietary.

Where DeepSeek R1 falls short
  • Slow (about 28 output tokens/s, per Artificial Analysis)
  • 164K context, smaller than 27.9% of the coding-agent requests we measured
Full DeepSeek R1 breakdown →
Where GPT-5.3-Codex falls short
  • Deprecated in Codex with ChatGPT sign-in
  • GPT-6.1 Sol scores far higher for about 20% more per request ($0.067 vs $0.055)
Full GPT-5.3-Codex breakdown →

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