GPT-6 vs Claude vs Gemini: Which AI Model for Which Task? (2026)

Every frontier model in late 2026 is good. The differences that decide which one you should use are practical: what inputs it takes, how much it costs at your volume, and how its reasoning is controlled. This comparison sticks to those.

We haven't published our own benchmark yet (it's in progress), so this page doesn't rank models on quality. Where it says one model is "for" something, that's the vendor's own positioning or a hard capability difference, and we say which.

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Quick reference

These numbers come straight from each vendor's documentation and update when our model registry does.

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How the three families differ

Inputs. This is the biggest hard difference.

• Gemini 3.8 Flash and 3.5 Flash-Lite: text, images, video, audio and PDFs
• Claude (current models): text, images and PDFs
• GPT-6: text and images (OpenAI serves audio through separate realtime and transcription models)

If your input is a meeting recording, a screen capture or a product video, Gemini is the only one of the three that takes it directly.

Context and output. All the flagship and mid-tier models take roughly a million input tokens, so context size alone rarely decides anything now. Output limits vary more: 128K tokens for GPT-6 and current Claude models, 65,536 for Gemini 3.8 Flash. That matters if you're generating very long documents or large code files in one call. Claude Haiku 4.5 is the outlier with a 200K input window.

Reasoning controls. All three reason internally and hide the raw reasoning. Each has its own dial:

Effort affects cost directly, because reasoning tokens are billed as output. See how to prompt reasoning models.

Tiers. Each vendor has a flagship, a balanced tier and a fast tier:

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Choosing by task

Hard reasoning, coding and long-running agents

Candidates: GPT-6 Astra, Claude Fable 5.1, Claude Opus 5.5

OpenAI recommends Astra for most reasoning workloads. Anthropic positions Fable 5.1 as its most capable model for demanding, long-horizon work, and Opus 5.5 for coding and agentic tasks at a lower price. Astra and Fable 5.1 are the same list price ($10 / $50); Opus 5.5 is $4 / $20.

For agents that make many tool calls, test at high effort (Claude's guidance puts xhigh as the best starting point for most coding and agentic use) and measure cost per completed task, not per request.

Everyday production work

Candidates: GPT-6 Sol, Claude Sonnet 5, Gemini 3.8 Flash

Sol and Sonnet 5 share a list price ($2 / $10). Gemini 3.8 Flash is cheaper during its promotion and still competitive after it. For writing, summarization, standard coding help and customer-facing chat, start here, not with the flagships.

High-volume classification and extraction

Candidates: GPT-6 Luna, Gemini 3.5 Flash-Lite, Claude Haiku 4.5

At millions of calls, price dominates. Luna is the cheapest current frontier model at list price. Pair any of these with structured outputs so every response parses. If a fast model fails a specific category of input, route just that category to a bigger one; see model routing.

Video, audio and mixed media

Candidate: Gemini 3.8 Flash

It's the only option of the three that takes video and audio directly. It handles up to about 3 hours of video at low resolution in its 1M window. Our Gemini guide covers timestamps and frame sampling.

Long documents

Candidates: any 1M-context model

Every flagship and balanced model fits a very large document set. What matters more is structure: label each document, tell the model where to look, and ask for citations. If the job is long output (a full report or a large file), prefer a 128K-output model.

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What it costs at scale

Here's a realistic workload: 100,000 requests, each with 2,000 input tokens and 500 output tokens. Costs are calculated from current list prices:

Two caveats. Reasoning tokens are billed as output, so at high effort your real output count can be several times the visible answer. And prompt caching and batch APIs can cut these numbers substantially for repeated prefixes and non-urgent work.

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Open-weight models

Open-weight models (Qwen, DeepSeek, Llama, Mistral, Gemma and others) make sense when data can't leave your infrastructure, or when you run enough volume that self-hosting beats API prices. Our Llama 3 and Mistral guides are older and kept for reference. A current open-weight guide is in progress.

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How to choose

1. Start from your inputs. Video or audio means Gemini. Otherwise all three families are in play.
2. Start cheap. Run your real task on the fast tier and the balanced tier first.
3. Test, don't guess. Take 20–50 real examples and run them on two or three candidates. Our Promptfoo tutorial sets this up in one config file.
4. Tune effort before changing models. A lower effort on a bigger model sometimes beats a smaller model at high effort, and it's a one-line change.
5. Recheck quarterly. Prices and lineups change every few months. The tables on this page update when they do.

Per-vendor prompting guides: OpenAI GPT-6 · Claude · Gemini