GPT-5.6 vs Claude Opus 5 vs Gemini 3.7 Flash vs Grok 4.6: Which AI Fits Which Job?

Asking which artificial-intelligence model is “best” sounds straightforward. In 2026, it is usually the wrong question.

OpenAI’s GPT-5.6 Sol, Anthropic’s Claude Opus 5, Google’s Gemini 3.7 Flash and xAI’s Grok 4.6 all sit near the front of the commercial model market. Yet they are designed around different trade-offs. One favours difficult professional and tool-using work, another long-running reasoning, another low-cost multimodal processing, and another web-connected agents.

This comparison focuses on the underlying API models available to developers as of 29 August 2026. That is not the same as comparing the ChatGPT, Claude, Gemini and Grok consumer apps. Subscriptions, usage limits, built-in tools and even the exact model deployed inside an app can differ.

GPT, Claude, Gemini and Grok: API specifications compared

Model	Context window	Native input	API price per 1M tokens	Strong starting point for
GPT-5.6 Sol	1.05 million	Text, images	$4 input / $20 output*	Complex professional and tool-using agents
Claude Opus 5	1 million	Text, images	$5 input / $25 output	Demanding coding and sustained reasoning
Gemini 3.7 Flash	1.048 million	Text, images, audio, video, PDFs	$0.75 input / $3.75 output*	Fast, high-volume multimodal work
Grok 4.6	500,000	Text, images	$2 input / $6 output**	Agents using web and X search

*The listed GPT-5.6 Sol rate is promotional through at least 21 November 2026. Gemini’s introductory rate ends on 31 December 2026. **Grok’s rate doubles when a prompt exceeds 200,000 tokens. OpenAI also applies higher rates above 272,000 input tokens. Prices exclude some tool calls and other charges.

Token prices are useful, but they are not perfectly comparable. Each provider tokenises text differently, and a model that solves a task with fewer attempts can cost less even when its advertised rate is higher.

Claude has a narrow reasoning lead—not a universal victory

Anthropic positions Claude Opus 5 for advanced coding, research and high-autonomy agentic work. It supports a one-million-token context window and up to 128,000 output tokens, giving it room for large codebases, long reports or multi-step workflows.

One useful independent reference is the Artificial Analysis Intelligence Index. At the highest evaluated reasoning settings, it gave Opus 5 a score of 63, compared with 61 for GPT-5.6 Sol and Grok 4.6, and 56 for Gemini 3.7 Flash. These are composite index points, not percentages correct.

The two-point lead is small, and the index is weighted toward English-language agentic, coding, scientific and general-reasoning tests. It does not fully capture writing style, safety, multilingual performance, latency or audio and video understanding. Opus is therefore a sensible first candidate for a difficult coding agent—not proof that it wins every task.

Its principal disadvantages are price and speed. At $5 per million input tokens and $25 per million output tokens, repeated or high-volume workloads can become expensive.

GPT-5.6 Sol is the broad professional all-rounder

GPT-5.6 Sol combines a 1.05-million-token context window with OpenAI’s widest professional toolset. Through the Responses API, it can use web and file search, code execution, a hosted shell, computer control, functions, image generation and Model Context Protocol connections.

That makes it a strong candidate when the job is not merely answering a question but collecting evidence, working across files and software, and producing a finished result. OpenAI’s own launch evaluations showed high scores on browsing, coding and computer-use tasks, although those figures are provider-reported rather than independent proof of superiority.

Its large window is not perfect memory. OpenAI’s published long-context tests show retrieval accuracy falling as material approaches the full window. The company’s system card also says factual errors have been reduced, not eliminated, and identifies “over-persistence”: an agent can continue pursuing a goal beyond the user’s intent or present insufficiently verified work as complete.

In practical deployments, powerful tools need narrow permissions, logs and human review.

Gemini 3.7 Flash wins on speed, price and media input

Google’s Gemini 3.7 Flash is the standout when an application needs to process large volumes of mixed media. It can natively accept text, images, audio, video and PDFs, while the other three models in this comparison accept text and images.

It also has by far the lowest introductory API price: $0.75 per million input tokens and $3.75 per million output tokens through the end of 2026. Independent testing by Artificial Analysis reports several hundred generated tokens per second under its test conditions, substantially faster than the other models here.

That combination suits jobs such as reviewing recorded meetings, classifying video, extracting information from document archives or running a high-volume customer-support pipeline. Gemini also supports code execution, search grounding, file search and structured outputs.

The trade-off is that Flash does not top the text-heavy reasoning composite. Google’s own model card warns about hallucinations, occasional slowdowns and continuing jailbreak-resistance work. Fast and inexpensive does not mean infallible.

Grok 4.6 is distinctive for web-connected agents

Grok 4.6 pairs competitive reasoning with built-in web search, X search, code execution and retrieval tools. Its $6-per-million output-token price below the long-prompt threshold is much lower than GPT-5.6 Sol or Claude Opus 5.

This makes it an interesting candidate for an agent that monitors public web information or X discussions and then performs analysis. But retrieval does not guarantee truth: the model can still misunderstand a source, miss context or repeat unreliable posts. Its 500,000-token window is also half the size of its closest rivals, although it remains ample for many workflows.

xAI’s model card says Grok 4.6 should not make autonomous high-stakes medical, legal, financial or safety decisions without expert oversight.

Why benchmark winners often disappoint in real work

A benchmark ranking can change with the prompt, reasoning budget, tools, software version and scoring method. The leading systems are now close enough that a two-point composite gap may matter less than an organisation’s own data and workflow.

A hospital summarising scanned records may value Gemini’s multimodal input. A software team running a long-lived coding agent may prefer Opus. A research workflow that needs many integrated tools may favour GPT-5.6 Sol. A social-media monitoring system may benefit from Grok’s X search.

The safest purchasing test is a small evaluation set drawn from real work. Measure answer quality, source accuracy, total completion time, human correction and full cost—including retries and tool calls. Do not send sensitive information until the provider’s retention, regional hosting and enterprise privacy terms have been checked.

So which model should you choose?

• Start with Claude Opus 5 for the hardest sustained coding and reasoning, if cost is secondary.
• Start with GPT-5.6 Sol for broad professional agents that must browse, use software and work across files.
• Start with Gemini 3.7 Flash for fast, inexpensive processing of documents, audio, images or video at scale.
• Start with Grok 4.6 when web and X retrieval are central and output cost matters.

For high-stakes factual decisions, the correct choice is none of them without source verification and accountable human review.

Reporting note

Model versions, promotional prices and benchmark results change quickly. Specifications and prices in this article were checked against current documentation on 29 August 2026. The Artificial Analysis result is an independent composite with its own weighting and uncertainty; provider benchmark claims are not directly comparable unless they use the same test harness.

For a comparison focused on running models on your own hardware, see Qwen, gpt-oss and Llama open-weight AI.

Sources and further reading

1. OpenAI: GPT-5.6 Sol model documentation
2. Anthropic: Claude model overview
3. Google: Gemini 3.7 Flash documentation
4. xAI: Grok 4.6 model documentation
5. Artificial Analysis: Intelligence Index
6. Artificial Analysis: benchmark methodology

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