Head to head · tested 27 Oct 2026

GPT-5.3-Codex vs GPT-6.1 Terra Pro: accuracy, speed and price

Similar accuracy; GPT-6.1 Terra Pro costs less per question.

OpenAIGPT-5.3-Codex82.5% score · #3$0.01 per question95 tok/sOpenAIGPT-6.1 Terra Pro76.2% score · #12$0.005 per question— tok/s

Verdict

Built from the measurements below

  • Accuracy is too close to call: GPT-5.3-Codex scored 82.5% and GPT-6.1 Terra Pro 76.2% on the same 80 questions, a gap within what chance alone could produce.
  • GPT-6.1 Terra Pro is 2.1× cheaper per question as billed ($0.005 vs $0.01), reasoning tokens included.
  • GPT-5.3-Codex starts answering sooner: 1.4 s to the first token, against 8.5 s for GPT-6.1 Terra Pro.
  • GPT-6.1 Terra Pro accepts more context: 1.1M tokens against 400k.

Which should you choose?

Choose GPT-5.3-Codex for a quicker first token. Choose GPT-6.1 Terra Pro for the lower cost per correct answer ($0.0065 against $0.013), shorter outputs (about 453 tokens per question against 699) and a 1.1M-token context window.

Accuracy gap +6.3 points (z = 0.98). A winner is called only when z is at least 1.96, the usual 95% threshold. Speed, latency and price need a 10% margin.

Head to head

Same questions, same prompt, same API

MeasureGPT-5.3-CodexGPT-6.1 Terra ProEdge
Verification scoreHigher is better82.5%76.2%Too close to call
Cost per questionLower is better$0.01$0.005GPT-6.1 Terra Pro
Output speedHigher is better95—Not measured
Time to first tokenLower is better1.4 s8.5 sGPT-5.3-Codex
End-to-end response timeLower is better5.2 s9.0 sGPT-5.3-Codex
Tokens per questionLower is better699453GPT-6.1 Terra Pro
Context windowHigher is better400k1.1MGPT-6.1 Terra Pro

Side by side

Bars are scaled to the larger of the two values. The winner on each measure is marked in bold.

Every measure

GPT-5.3-Codex first, GPT-6.1 Terra Pro second

Verification scorehigher is better
GPT-5.3-Codex82.5%
GPT-6.1 Terra Pro76.2%
Cost per questionlower is better
GPT-5.3-Codex$0.01
GPT-6.1 Terra Pro$0.005
Output speedhigher is better
GPT-5.3-Codex95
GPT-6.1 Terra Pro—
Time to first tokenlower is better
GPT-5.3-Codex1.4 s
GPT-6.1 Terra Pro8.5 s
End-to-end response timelower is better
GPT-5.3-Codex5.2 s
GPT-6.1 Terra Pro9.0 s
Tokens per questionlower is better
GPT-5.3-Codex699
GPT-6.1 Terra Pro453
Context windowhigher is better
GPT-5.3-Codex400k
GPT-6.1 Terra Pro1.1M

Score vs. cost per question

Both models in color, every other model in grey

Most attractive quadrant0255075100$0.00001$0.0001$0.001$0.01$0.10Cost per question (USD, log) →Verification score (%) →Qwen3.8 2.4T A95BCommand AReka Flash 3Gemma 4 26B A4BGemma 4 31BQwen3.5 397B A17BHermes 4 405BMuse WonderGrok 4.7o3gpt-oss-20bMinistral 3 3B 2512Nova Micro 1.0Phi 4Granite 4.2 8BHy3GLM 5.3 FlashSolar Pro 4Trinity Large ThinkingMiniMax M3Mistral Large 4Claude Opus 5.5GPT-6.1 SolGemini 4 ArgonGPT-5.4GPT-6 Lunagpt-oss-120bLlama 4 Maverick

Specifications and pricing

From OpenRouter's catalogue, plus the leaderboard's test dates

SpecificationGPT-5.3-CodexGPT-6.1 Terra Pro
CreatorOpenAIOpenAI
Released24 Feb 202626 Oct 2026
Weights——
ReasoningYesYes
Inputtext, image, filefile, image, text
Outputtexttext
Context window400k tokens1.1M tokens
Knowledge cutoff——
List price in / out per 1M$1.75 / $14$1 / $5
Blended price per 1M (3:1)$4.81$2.00
Cheapest hostAzure · $1.75 / $14OpenAI · $0.5 / $2.5
Hosts on OpenRouter22
Last tested27 Oct 202627 Oct 2026

Compare any two models →

GPT-5.3-Codex vs GPT-6.1 Terra Pro: frequently asked questions

Is GPT-5.3-Codex better than GPT-6.1 Terra Pro?
On accuracy they are statistically tied: GPT-5.3-Codex scored 82.5% and GPT-6.1 Terra Pro 76.2% on the same 80 questions from open datasets. Similar accuracy; GPT-6.1 Terra Pro costs less per question.
Which is cheaper, GPT-5.3-Codex or GPT-6.1 Terra Pro?
GPT-6.1 Terra Pro is cheaper: $0.005 per question as billed, against $0.01. List prices: GPT-5.3-Codex $1.75 in / $14 out, GPT-6.1 Terra Pro $1 in / $5 out per million tokens.
Which has the larger context window, GPT-5.3-Codex or GPT-6.1 Terra Pro?
GPT-6.1 Terra Pro, with 1.1M tokens against 400k.