February 12, 2026 might go down as one of the most consequential days in AI history. OpenAI and Google both dropped major model releases within hours of each other — GPT-5.3-Codex-Spark and Gemini 3 Deep Think — each targeting radically different aspects of what AI can do. One is built for speed. The other is built for depth. Together, they paint a picture of an industry splitting into specialized lanes at breakneck pace.
If you're a developer, researcher, or just someone trying to keep up with the AI arms race, here's everything you need to know about both launches — and what they mean for you.
GPT-5.3-Codex-Spark: Real-Time Coding at 1,000+ Tokens Per Second
OpenAI's GPT-5.3-Codex-Spark is the smaller, faster sibling of GPT-5.3-Codex — and it's the first model explicitly designed for real-time coding assistance. The headline number: over 1,000 tokens per second, with ultra-low latency that makes AI-assisted coding feel genuinely instantaneous.
This isn't just an incremental speed bump. OpenAI partnered with Cerebras to run Codex-Spark on the Wafer Scale Engine 3, a custom chip architecture that eliminates traditional bottlenecks in inference. The result is a model that doesn't just think fast — it responds fast, with 80% reduced roundtrip overhead, 30% lower per-token overhead, and 50% faster time-to-first-token compared to standard Codex.
How It Works
Codex-Spark uses a persistent WebSocket connection rather than the typical request-response pattern. This means the model maintains a live session with your editor, delivering completions and edits as a continuous stream rather than discrete API calls. For developers, this translates to an experience closer to pair programming with a human — responses arrive as you type, not after you wait.
The model ships with a 128k context window (text-only, no multimodal) and is optimized for lightweight edits and minimal targeted changes. Think of it as the scalpel in OpenAI's coding toolkit: precise, fast, and purpose-built for the kind of small-to-medium edits that make up 90% of daily coding work.
Where to Use It
Codex-Spark is available as a research preview for ChatGPT Pro users. You can access it through:
- The Codex app (OpenAI's dedicated coding environment)
- CLI (command-line interface for terminal-first developers)
- VS Code extension (inline completions and edits)
Early benchmarks show strong performance on SWE-Bench Pro and Terminal-Bench 2.0, two of the most rigorous real-world coding evaluation suites. This matters because synthetic benchmarks often don't capture how models perform on actual codebases — SWE-Bench Pro tests against real GitHub issues, and Terminal-Bench 2.0 evaluates command-line task completion.
If you've been following the evolution of AI coding tools like Replit Agent 3, Codex-Spark represents a fundamentally different approach: instead of an autonomous agent that builds entire apps, it's a co-pilot optimized for the tight feedback loop of editing existing code.
Gemini 3 Deep Think: When AI Needs to Actually Reason
While OpenAI went all-in on speed, Google went all-in on depth. Gemini 3 Deep Think is a major upgrade to Google's specialized reasoning mode, and the benchmarks are genuinely staggering.
Let's start with the numbers:
- 48.4% on Humanity's Last Exam (without tools) — a benchmark designed to contain questions that stump every existing AI
- 84.6% on ARC-AGI-2 — the abstract reasoning benchmark that tests genuine pattern recognition, not memorization
- 3,455 Elo on Codeforces — placing it among the top competitive programmers in the world
- IMO 2025 gold medal level — solving problems from the International Mathematical Olympiad at the highest tier
These aren't incremental improvements. An 84.6% score on ARC-AGI-2 and a Codeforces rating of 3,455 suggest Deep Think can handle genuinely novel problems — the kind where you can't pattern-match to training data and actually need to reason through unfamiliar territory.
Who It's For
Deep Think isn't a general-purpose chatbot upgrade. Google has positioned it squarely for science, research, and engineering challenges. The model is available to Google AI Ultra subscribers, with API access limited to select researchers and enterprises.
Google highlighted two real-world use cases that demonstrate the model's strengths:
- Math paper flaw detection — Deep Think can analyze mathematical proofs and identify logical errors, gaps in reasoning, or incorrect assumptions. For academic researchers, this is a genuine productivity multiplier.
- Semiconductor crystal growth optimization — Working with materials scientists, Deep Think has been used to optimize parameters in crystal growth processes, a domain where the search space is enormous and traditional simulation is expensive.
The pattern is clear: Google is betting that the highest-value AI applications aren't about speed — they're about solving problems that humans struggle with, even given unlimited time. This aligns with a broader trend we've covered in our analysis of how AI agents are pushing ethical boundaries — as models get more capable, the questions about where and how to deploy them become more complex.
Head-to-Head Comparison: Spark vs Deep Think
These two models aren't really competitors — they're optimized for entirely different use cases. But since they launched on the same day, a comparison is inevitable. Here's how they stack up:
| Feature | GPT-5.3-Codex-Spark | Gemini 3 Deep Think |
|---|---|---|
| Primary Focus | Real-time coding assistance | Deep reasoning & research |
| Speed | 1,000+ tokens/sec | Slower (extended thinking) |
| Context Window | 128k tokens | Not disclosed |
| Modality | Text-only | Multimodal reasoning |
| Hardware | Cerebras Wafer Scale Engine 3 | Google TPUs |
| Access | ChatGPT Pro (research preview) | AI Ultra subscribers + select API |
| Key Benchmark | SWE-Bench Pro, Terminal-Bench 2.0 | ARC-AGI-2 (84.6%), Codeforces (3455 Elo) |
| Best For | Daily coding, quick edits, IDE integration | Math, science, complex engineering |
| Connection | Persistent WebSocket | Standard API |
The Bigger Picture: AI Is Specializing Fast
The simultaneous launch of Codex-Spark and Deep Think signals something important about where the AI industry is heading: the era of the one-model-fits-all approach is ending.
A year ago, the conversation was about which model was "best" — GPT-4 vs Claude vs Gemini in a generic head-to-head. Today, the question is which model is best for what. Need to edit code at the speed of thought? Codex-Spark. Need to verify a mathematical proof or optimize a materials science experiment? Deep Think. Need to build an entire app from scratch? Maybe something like Replit Agent 3.
This specialization trend has massive implications for developers and businesses. Instead of picking one AI provider and using it for everything, the smart play is becoming an AI orchestrator — routing different tasks to different models based on their strengths. At Serenities AI, this is exactly the kind of multi-model thinking we help teams adopt, because the cost and capability differences between models can be dramatic (as we've broken down in our analysis of AI agent costs).
What Else Happened Today
As if two major model launches weren't enough, February 12 also brought:
Anthropic's $30 Billion Raise at $380 Billion Valuation
Anthropic, the maker of Claude, closed a staggering $30 billion funding round at a $380 billion valuation. This makes Anthropic one of the most valuable private companies in the world and signals that investors see the AI market as far from saturated. The capital will likely fund continued development of Claude models and expansion of their safety research program.
MiniMax M2.5 Hits 80.2% on SWE-bench Verified
Chinese AI lab MiniMax quietly dropped M2.5, which scored 80.2% on SWE-bench Verified — a remarkable result that puts it in the top tier of coding models globally. The competitive pressure from Chinese labs continues to accelerate, pushing the entire field forward.
What This Means for Developers
If you're a developer trying to figure out what to do with today's news, here's the practical takeaway:
- Try Codex-Spark if you have ChatGPT Pro. The speed improvements are not subtle — 1,000+ tokens per second with persistent WebSocket connections changes how AI-assisted coding feels. If you're currently using Copilot or Cursor, Codex-Spark's speed advantage is worth testing.
- Watch Deep Think for research-heavy work. If your work involves math, science, or complex engineering problems, Gemini 3 Deep Think's reasoning capabilities are in a different league. The ARC-AGI-2 and Codeforces scores suggest genuine reasoning ability, not just pattern matching.
- Start thinking multi-model. The days of picking one AI tool are numbered. Build workflows that route simple coding tasks to fast models (Codex-Spark) and complex reasoning tasks to deep models (Deep Think or Claude). This orchestration approach will deliver better results and often lower costs.
- Keep an eye on open-source alternatives. MiniMax M2.5's SWE-bench score shows that the gap between proprietary and open models continues to shrink. Don't lock yourself into one ecosystem.
The AI Model War Is Just Getting Started
Today's launches from OpenAI and Google represent a fundamental shift in how AI companies compete. Rather than fighting over who has the "smartest" general-purpose model, they're racing to build the best specialized models for specific domains.
OpenAI is betting that speed wins in coding — that developers want AI that feels as fast as autocomplete, not AI that takes 30 seconds to think. Google is betting that depth wins in research — that scientists and engineers need AI that can reason through genuinely hard problems, even if it takes longer.
Both bets are probably right. And that's what makes this moment so exciting. The AI model war isn't a zero-sum game anymore — it's an expanding universe of specialized capabilities that, combined, are making AI useful in ways that weren't possible even six months ago.
For now, the winners are the developers and researchers who get access to these tools. The question is how quickly they'll trickle down from premium tiers to everyone else.
Frequently Asked Questions
What is GPT-5.3-Codex-Spark and how is it different from regular Codex?
GPT-5.3-Codex-Spark is a smaller, faster version of GPT-5.3-Codex, specifically optimized for real-time coding. While standard Codex handles complex, multi-file tasks, Spark focuses on lightweight edits and quick completions at 1,000+ tokens per second. It runs on Cerebras Wafer Scale Engine 3 hardware and uses persistent WebSocket connections for near-instantaneous response times.
Who can access Gemini 3 Deep Think?
Gemini 3 Deep Think is available to Google AI Ultra subscribers. API access is currently limited to select researchers and enterprise partners. Google has positioned it as a specialized tool for science, research, and engineering rather than a general-purpose chatbot upgrade.
Can I use both GPT-5.3-Codex-Spark and Gemini 3 Deep Think?
Yes, and that's actually the recommended approach. These models excel at different tasks — Codex-Spark for fast coding edits and Deep Think for complex reasoning. Many developers are adopting multi-model workflows that route tasks to the best-suited model, optimizing for both speed and quality.
How does Codex-Spark compare to GitHub Copilot or Cursor?
Codex-Spark's main advantage is raw speed — 1,000+ tokens per second with 80% reduced roundtrip overhead. Its persistent WebSocket connection also provides a smoother experience than traditional request-response patterns. However, Copilot and Cursor offer deeper IDE integrations and have been battle-tested longer. The best choice depends on your workflow preferences.
What does Anthropic's $30B raise mean for the AI market?
Anthropic's $30 billion raise at a $380 billion valuation signals massive investor confidence in the AI market's growth potential. It means Anthropic has the resources to compete aggressively with OpenAI and Google, which should accelerate innovation and keep pricing competitive for end users. It also validates the "multiple winners" thesis — investors don't see AI as a winner-take-all market.