ChatGPT vs Gemini for coding: Codex wins 54.1 to 53.2 on the coding agent index, at about a quarter of the cost per task

Is ChatGPT or Gemini better for coding?

ChatGPT is better for coding by a hair, and OpenAI's cheaper model is the better buy. On the Artificial Analysis Coding Agent Index, read 25 October 2026, Codex with the bigger GPT Astra scored 54.7 but spent $7.04 a task. Sol got within a point of it for $1.50. Argon sits between them on cost.

The index puts each lab's own agent through three tests, with 290 tasks in all. FrontierCode comes from Cognition, and it asks if a maintainer would merge the change that the agent made to a real repo. Terminal-Bench 4.0 gives it 66 hard jobs to do in a shell. SWE-Atlas QnA, from Scale AI, asks it 124 questions about how a codebase works.

Gemini wins one of the three. Antigravity with Argon passed 56.1% of the terminal jobs, which is ahead of Codex with Sol at 53.0% and even of Codex with GPT Astra at 55.6%. Codex wins the codebase questions by 4.8 points. FrontierCode is a draw, at 49.4% to 48.6%. Artificial Analysis says that its index "is useful for quick comparison, but it should be read alongside the per-eval breakdowns." A gap of 0.9 points is just that kind of case.

Is ChatGPT or Gemini better for Python?

Gemini is better for Python on the one Python test that Artificial Analysis runs. SciCode asks for Python functions to solve 80 lab problems, and Gemini 4 Argon passed 61.8% of them at high effort. GPT Sol passed 54.2% of them at max effort, and GPT Astra 56.5%.

Even Google's small Gemini Flash beats GPT Sol on it, 56.6% to 54.2%. SciCode is science code, though. Read it as a guide for numeric work, not for a Django app. On FrontierCode, which is the repo test, Sol leads Argon by 5.6 points when both of them run in the harness that Artificial Analysis built.

Which coding agent comes with a $20 plan, Codex or Antigravity?

Both of the $20 plans come with a coding agent as of October 2026: ChatGPT Plus has Codex, and Google AI Pro has Antigravity and Jules. OpenAI charges $20 a month for Plus. Google charges $19.99 for its plan.

OpenAI is the only one of the two that gives you a number. Its Codex pricing page estimates 15 to 160 local messages every five hours on Sol, and it pitches Plus as the plan to "Power a few focused coding sessions each week." A cloud task can use more of that than a local one does.

Google gives you adjectives. Its Antigravity plans page promises Pro "a high, generous quota, refreshed every five hours until the weekly limit is reached." That's all it says. The cheaper Google AI Plus, at $4.99 on Google's plans page, doesn't list a coding agent at all.

Gemini CLI isn't Google's consumer agent any more. Its maintainers wrote on GitHub on 19 May 2026: "On June 18, 2026, Gemini CLI will stop serving requests for Google AI Pro and Ultra". Free users went the same day, and Google points all of them to Antigravity CLI. A ChatGPT vs Gemini CLI test from before June doesn't show what a personal account gets now.

Which is cheaper for coding, ChatGPT or Gemini?

ChatGPT is cheaper for coding, by about 4x for each agent task. In the Coding Agent Index, Codex with GPT Sol cost $1.50 on an average task and Antigravity with Gemini 4 Argon cost $5.86, though the two of them have the same price per token today.

Google's Gemini API pricing page charges $2 per million input tokens and $10 per million output for Argon through 30 November 2026. OpenAI's API pricing charges the same $2 and $10 for Sol. Argon costs more because it reads more. It took in about 13.8 million tokens on each task, and Sol took in 3.9 million.

The gap gets wider on 1 December. That's when Argon moves to $4 and $20. Every one of its rates doubles, cached input included. By our arithmetic, $5.86 times two, the same runs would then cost about $11.72 a task, which is close to 8 times what Sol costs. On the plans, it's a tie at $20 against $19.99.

Is ChatGPT or Gemini faster for coding?

ChatGPT gets agent tasks done faster. It took Codex with GPT Sol 23.7 minutes on an average task in the index. It took Antigravity with Gemini 4 Argon 34.7.

Sol is quicker on a single answer too. On the model leaderboard, Sol at high effort took a median 45.5 seconds to go from your prompt to a full reply, and Argon at high took 63.2. Argon writes faster once it starts, at 81 tokens a second to Sol's 53. Most of its wait is the thinking it does before the first word.

How do ChatGPT and Gemini compare on SWE-bench?

SWE-bench can't rank the current ChatGPT and Gemini models, because neither of its boards lists them. The official SWE-bench Verified board has no entry after 26 February 2026. Scale's SWE-bench Pro board stops at GPT-5.4 and Gemini 3.1 Pro.

On that board, read 25 October 2026, the best of the OpenAI entries scored 59.10% and the best of the Google ones 46.10%. OpenAI led by 13 points. Both of those models are older than the ones in Codex and Antigravity today, so treat a SWE-bench table as history.

Should you use ChatGPT or Gemini for coding?

Use ChatGPT when you pay per task or want a published limit, and Gemini when you do terminal work or Python for science.

Neither one of them wins every row, so test both on your own repo first. For the wider match on writing and research, read Gemini vs ChatGPT. For Anthropic's agent on the same index, Claude vs Gemini for coding puts Claude Code against Antigravity. If you want other coding tools in the mix, ChatGPT alternatives for coding covers them.

Whizi fits one narrow case: you want GPT Sol and a Gemini model to answer the same coding question before you pay for a plan. Side by side chats come with Whizi Pro at $29.99 a month, and credits per message vary by model and can change. Whizi is a chat app, so for edits across a repo you'll still want Codex or Antigravity. The coding guide shows which coding models each plan reaches.