Two of the most talked-about AI releases of February 2026 landed within a week of each other, and both are gunning for developers. GPT-5.3-Codex Spark (not to be confused with the regular GPT-5.3 Codex) arrived on February 12 as a research preview, powered by Cerebras silicon and clocking over 1,000 tokens per second. Claude Opus 4.6 was released a week earlier, on February 5, with a 1M-token context window and benchmark numbers that left the community in awe. But its more expensive (and faster) sibling, Claude Opus 4.6 Fast Mode, is what we’re discussing today. If you’re trying to figure out where each one fits in your workflow, this breakdown between GPT-5.3 Spark vs Claude Opus 4.6 Fast should help.

GPT-5.3-Codex Spark vs Claude Opus 4.6 Fast – Overview

Before diving into the numbers, it’s worth being clear about something: this is NOT a direct comparison between GPT-5.3 Codex and Claude Opus 4.6, but the Codex Spark and Opus 4.6 Fast.

For GPT-5.3 Codex vs Claude Opus 4.6 comparison, follow this:

Back to the comparison, GPT-5.3-Codex Spark is a smaller, distilled version of GPT-5.3-Codex. Its entire design philosophy centers on speed. Running on Cerebras’ third-generation Wafer-Scale Engine, a chip containing 4 trillion transistors and 900,000 AI-optimized cores, Spark was built to eliminate the latency that breaks developer flow state. The idea is that instead of writing a prompt, waiting 30 to 60 seconds, reviewing output, and cycling back, developers can steer the model in real time as code appears. OpenAI describes Codex as having already crossed 1 million weekly active users, which gives a sense of the developer appetite this model is feeding into.

Claude Opus 4.6, by contrast, is Anthropic’s flagship general-purpose and agentic model, the top of the Opus line. It’s built for sustained, complex, long-horizon work: large codebases, multi-step reasoning, enterprise workflows, and tasks that require holding enormous amounts of context without losing the thread. Released just a week before Spark, on February 5, it also came with some of the most significant capability jumps Anthropic has shipped in a single release. These aren’t directly competing for the same use case, but they do overlap meaningfully in agentic coding, and that’s where the comparison gets most interesting.

GPT-5.3-Codex Spark vs Claude Opus 4.6 Fast – Benchmark Breakdown

Here’s how the two models stack up across the key evaluations that matter most for developers:

A few things stand out. On Terminal-Bench 2.0, the benchmark that specifically tests a coding agent’s ability to use terminal tools across complex multi-step tasks, Opus 4.6 leads at 65.4% versus Spark’s 58.4%. That gap reflects the trade-off at the core of Spark’s design: it sacrifices some raw problem-solving depth in exchange for response speed. For comparison, the full GPT-5.3-Codex scores 77.3% on Terminal-Bench 2.0, which shows just how much Spark gives up to hit that 1,000 tokens-per-second mark. It’s worth noting that benchmark figures for Spark are based on OpenAI’s own published data from the research preview, and independent verification is still pending.

Where GPT-5.3-Codex Spark Pulls Ahead

Speed is not a minor advantage. It’s a fundamentally different interaction model, and for certain workflows it changes everything.

When Spark runs on Cerebras hardware, it also benefits from an 80% reduction in per-roundtrip client-server overhead, which OpenAI achieved by rewriting key parts of the inference stack and switching to WebSocket streaming. That means the near-instant feel isn’t just about token generation speed. It’s the entire loop from prompt submission to seeing code arrive on screen that’s been compressed. For developers who spend most of their day in tight edit-test cycles, that compression is genuinely meaningful.

The tasks where Spark genuinely shines over Opus 4.6 include:

• Rapid iteration on focused edits: Spark is tuned to make minimal, targeted changes rather than rewriting whole sections, which is exactly what you want when refining a component in real time.
• Revising plans mid-flight: You can redirect Spark as it’s generating, steering it toward a different approach without waiting for a full response to finish.
• Styling and layout work: Cerebras describes Spark as particularly well-suited for quickly visualizing UI changes and testing interface adjustments.
• Short-context agentic loops: For tasks that fit within 128K tokens, Spark’s interactive speed turns the traditional “prompt and wait” cycle into something closer to pair programming.

Where Claude Opus 4.6 Fast Pulls Ahead

Opus 4.6 is built for the kind of work where Spark would genuinely struggle, and the gap is significant in certain areas.

The context window story alone is substantial. Opus 4.6 scores 76% on MRCR v2, the needle-in-a-haystack retrieval test with 8 needles buried across 1M tokens. Its predecessor managed just 18.5% on the same test. Spark’s 128K limit means it simply can’t be in the conversation for tasks involving large codebases, lengthy regulatory documents, or multi-document research sessions.

Where Opus 4.6 has a clear lead, and where the data backs it up:

• Long-horizon agentic coding: 65.4% on Terminal-Bench 2.0, ahead of Spark and better than GPT-5.2-Codex’s 64.7%, while also handling more tokens per task.
• Multi-agent coordination: Claude Code now supports agent teams, where multiple independent Claude instances work in parallel on different components of a large codebase, with each agent holding its own context window.
• Enterprise and knowledge work: On GDPval-AA, which measures real-world professional tasks in finance and legal domains, Opus 4.6 holds a 144-point Elo lead over GPT-5.2.
• BrowseComp: Opus 4.6 tops this benchmark for locating hard-to-find information through multi-step search, which makes it significantly more capable for research-heavy workflows.
• Scientific and reasoning tasks: Scores of 91% on GPQA Diamond and 68.8% on ARC-AGI-2 (up from 37.6% for its predecessor) show that Opus 4.6’s improvements aren’t just about coding.
• Massive context fidelity: The MRCR v2 score of 76% at 1M tokens versus competitors that degrade sharply past 32K gives Opus 4.6 a practical advantage for large document and codebase work.

GPT-5.3-Codex Spark vs Claude Opus 4.6 Fast Pricing & Accessibility

This is where the two models diverge quite sharply, and it matters if you’re trying to decide which one to actually use today.

GPT-5.3-Codex Spark launched as a research preview on February 12, 2026. Access is currently limited to ChatGPT Pro subscribers ($200/month) through the Codex app, CLI, and VS Code extension. API access is rolling out only to select design partners, and there’s no published pricing for general API use yet. Spark also runs on separate rate limits from standard ChatGPT usage, and those limits can tighten when Cerebras hardware demand is high. OpenAI has signaled plans to expand access beyond Pro users, but there’s no confirmed timeline on that rollout.

Claude Opus 4.6 is broadly available immediately. It’s accessible via the Anthropic API, Claude.ai Pro ($20/month), Team plans, and major cloud platforms, including Amazon and Google Cloud.

API pricing is $5 per million input tokens and $25 per million output tokens (unchanged from Opus 4.5), with fast mode (a high-speed configuration offering up to 2.5x faster responses) available in research preview at premium rates of $30 per million input tokens and $150 per million output tokens for prompts ≤200K tokens (scaling to $60/$225 for >200K contexts). The 1M-token context window is accessible in beta via the context-1m-2025-08-07 beta header in API calls, with the standard context window sitting at 200K tokens for general use.

The Bottom Line

So, what do we get from the above breakdown? Spark is optimized for speed and excels in interactive, real-time coding, making it ideal for rapid iteration and quick edits. Its performance, exceeding 1,000 tokens per second on Cerebras hardware, offers a notable advantage during its research preview. In contrast, Opus 4.6 is designed for complex, long-context tasks such as multi-file codebases and enterprise workflows, providing greater reliability and accessibility. Most developers will benefit from using both models. Although, of course, each should serve a different purpose.