Google Intensifies the AI Coding Price Race

ByMason Reed

November 11, 2026

Gemini 4 Carbon is generally available at an introductory rate as Anthropic, OpenAI and open-source developers expand the tools competing for software teams.

Google has made Gemini 4 Carbon generally available through its API, adding another high-end model for developers building software and AI services. The introductory price is $2 per million input tokens and $10 per million output tokens through November 30, 2026. Google’s changelog lists the model as `gemini-4-carbon`.

Google describes Carbon as its strongest model and aims it at demanding technical work, including complex coding. The Verge reported that Google is also previewing an Antigravity “swarm” feature, intended to coordinate a team of agents from one prompt. That capability remains a preview, rather than a broadly available product feature.

The price comparison is notable, but it is not a complete measure of value. Anthropic lists Claude Fable 5.2 at $8 per million input tokens and $40 per million output tokens. Claude Mythos 5.2 is available only to organizations verified through Anthropic programs. Both models have a one-million-token context window and a maximum output of 128,000 tokens. Anthropic’s optional fast mode costs $16 per million input tokens and $80 per million output tokens; the company says it can generate tokens up to 2.5 times faster.

OpenAI’s current API pricing lists GPT-6 Astra at $10 per million input tokens and $50 per million output tokens under standard processing. Its Ultrafast mode is available through the Responses API with global processing and U.S. data residency, but not EU or other regional inference-residency options. For firms handling sensitive information, that limitation may matter as much as price or speed.

Small businesses should test costs on their own workloads before switching models. Long prompts, repeated agent calls and large outputs can quickly increase a bill, and Google’s introductory pricing has a firm end date. A useful comparison should measure quality, reliability and the number of calls required—not just the advertised rate.

Open-source developer tools are evolving alongside the models. Python project `pydantic-ai` released version 2.67.0 with SubAgents support under durable execution and input-sanitization fixes. Browser automation project `browser-use` released version 1.0.0 with a new engine: a persistent Python REPL that works directly with Chrome DevTools Protocol. One report says the rewrite removed 136,866 lines of code, though that figure alone says little about performance or reliability.

OpenAI has also agreed to acquire Astral, developer of the open-source Python tools uv, Ruff and ty. The tools handle environment and dependency management, linting and formatting, and type checking. OpenAI says it intends to continue supporting them as open source after closing; Astral’s team is expected to join the Codex group. Financial terms were not disclosed. The deal would bring familiar parts of the Python workflow closer to Codex, but its long-term significance will depend on how the projects are maintained.

Security deserves equal attention when teams try coding agents and generated apps. Recent reports described Claude Code settings files exposing passwords and API keys, and 35,000 Grok Build apps accessible to the public, some containing confidential information. These reports do not establish flaws in the products covered here. They do reinforce the need to check permissions, scan for secrets and review generated applications before deployment.

Gemini 4 Carbon is worth testing for teams already using Google’s API, particularly during its introductory-price period. Anthropic offers another option for technical work, while projects such as `pydantic-ai` and `browser-use` provide new ways to experiment with agent workflows. The practical choice depends on what performs reliably, fits the budget and keeps company data under control.

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