Gemini 3.1 Pro, Opus 4.6, and Codex 5.3: A Technical Breakdown of Three Models, Three #1 Positions

Key Takeaways

• Gemini 3.1 Pro hits 80.6% on SWE-Bench Verified (first model above 80%) by combining sparse MoE routing with Deep Think inference-time reasoning and Thought Signatures for multi-step context persistence.
• Claude Opus 4.6 leads real-world developer preference (GDPval-AA Elo 1633) through adaptive thinking effort levels and Agent Teams orchestration—a behavioral difference that shows up as collaborative coding rather than autonomous execution.
• GPT-5.3 Codex dominates terminal work (Terminal-Bench 77.3%) through self-bootstrapped training and token-efficient patching, but developers report it needs more babysitting on broad tasks.
• The benchmark leaderboard is now split three ways. The practical question isn’t which model is best—it’s which architectural bets matter for your workflow.

The Architecture Divergence

These three models don’t just score differently. They’re built on fundamentally different technical bets. Understanding the architecture explains why each model excels where it does.

Gemini 3.1 Pro: Sparse MoE + Deep Think + Thought Signatures

Gemini 3.1 Pro’s architecture rests on three mutually reinforcing innovations. Each addresses a different bottleneck in agentic coding.

Sparse Mixture of Experts

Gemini’s transformer backbone uses Mixture of Experts (MoE) layers. Instead of activating the entire network for every token, a gating router selects a small subset of expert sub-networks (e.g., 2 out of 16) per token. The rest stay idle.

This is how Gemini can have an enormous total parameter count while keeping per-token inference cost low. At $2/$12 per million tokens, 3.1 Pro is roughly 2.5x cheaper on input and 2x cheaper on output than Opus 4.6 ($5/$25). The MoE architecture is the enabling factor—you get frontier capability at a fraction of the compute cost because most of the network doesn’t fire per token.

Deep Think: Inference-Time Compute Scaling

Deep Think is Gemini’s reasoning mode. Rather than generating a single chain-of-thought, it explores multiple hypotheses in parallel and uses an internal verifier (codenamed Aletheia) to catch logical errors mid-chain.

Aletheia follows a Generator-Verifier-Reviser (GVR) loop:

1. Generator produces a candidate solution
2. Verifier checks the solution using natural language reasoning, flagging flaws or hallucinations
3. Reviser patches the solution or restarts from scratch if fundamentally flawed

This separation of generation from verification is critical. Google’s researchers found that explicitly separating verification helps the model recognize errors it overlooks during generation. In practice, this means Deep Think doesn’t just “think longer”—it debates with itself and discards bad reasoning paths.

Gemini 3.1 Pro introduces a new MEDIUM thinking budget level alongside LOW and HIGH, giving developers a cost/speed/accuracy dial:

from google import genai

client = genai.Client()

# Use MEDIUM thinking for balanced cost/accuracy
response = client.models.generate_content(
    model="gemini-3.1-pro",
    contents="Analyze this codebase for race conditions in the connection pool",
    config={
        "thinking_config": {"thinking_budget": "MEDIUM"},
    },
)

On Deep Think’s highest setting, Gemini 3 achieved 84.6% on ARC-AGI-2 and 95.1% accuracy on IMO-Proof Bench Advanced—with 100x less compute than its July 2025 version. That compute efficiency gain is the real story: inference-time reasoning is becoming cheaper fast.

Thought Signatures: Persistent Reasoning Across Turns

Thought Signatures solve a specific problem in multi-step agentic tasks: context drift. When a model performs a multi-turn function-calling workflow (e.g., “check the flight status, and if delayed, book a taxi”), the reasoning context from step 1 can degrade by step 3.

Thought Signatures are encrypted representations of the model’s internal reasoning state, returned alongside each response. You pass them back verbatim in the next turn:

import google.genai as genai

client = genai.Client()

# Step 1: Initial reasoning produces a thought signature
response = client.models.generate_content(
    model="gemini-3.1-pro",
    contents="Check flight status for AA100 and book a taxi if delayed.",
    config={"thinking_config": {"thinking_budget": "MEDIUM"}},
)

# The response includes function_call with a thought_signature
# Step 2: Pass the signature back to preserve reasoning context
# When sending the function result back, include the thought_signature
# from the previous response EXACTLY as received

# For Gemini 3 models, omitting the thought_signature on function
# calls returns a 400 validation error

For Gemini 3 models, passing back thought signatures during function calling is mandatory—omitting them returns a 400 validation error. This isn’t optional ergonomics; it’s a hard contract that reasoning state must persist across tool calls. The practical effect: multi-step agents don’t lose their train of thought between actions.

Claude Opus 4.6: Adaptive Thinking + Agent Teams

Opus 4.6 takes a different architectural bet. Rather than sparse routing at the model level, Anthropic focused on dynamic reasoning allocation and multi-agent coordination.

Adaptive Thinking

Previously, extended thinking was binary—on or off with a fixed token budget. Opus 4.6 replaces this with adaptive thinking that dynamically decides when deeper reasoning helps. Four effort levels control the behavior:

import anthropic

client = anthropic.Anthropic()

# Adaptive thinking: model decides reasoning depth
response = client.messages.create(
    model="claude-opus-4-6-20260205",
    max_tokens=16000,
    thinking={
        "type": "enabled",
        "budget_tokens": 10000,
    },
    messages=[{"role": "user", "content": "..."}],
)

# Or use effort-based control
response = client.messages.create(
    model="claude-opus-4-6-20260205",
    max_tokens=16000,
    thinking={"type": "adaptive"},  # Auto-selects effort level
    messages=[{"role": "user", "content": "..."}],
)

At high (the default), Claude almost always engages deep reasoning. At low, it skips thinking for simple queries. This directly reduces latency and cost on straightforward tasks while preserving depth when needed.

Agent Teams: Multi-Agent Orchestration

The most architecturally significant Opus 4.6 feature isn’t in the model itself—it’s the Agent Teams system in Claude Code. A lead agent spawns specialist agents (each with its own context window), assigns subtasks, and synthesizes results with peer-to-peer communication:

# Agent teams spawn through multiple backends
# Example: tmux-based multi-agent session
CLAUDE_CODE_TEAM_NAME="refactor-auth"
CLAUDE_CODE_AGENT_ID="agent-001"
CLAUDE_CODE_AGENT_TYPE="backend"

# Lead agent coordinates via file-based messaging:
# ~/.claude/teams/{team-name}/messages/{session-id}/

In a demonstration, an agent team generated 100,000 lines of working C compiler code that boots Linux on x86, ARM, and RISC-V—in roughly 8 hours. The practical pattern: one agent writes tests, another handles implementation, a third reviews security, all simultaneously.

Context Compaction

For long-running agentic sessions, Opus 4.6 introduces server-side context compaction. When context approaches the window limit, the API automatically summarizes older exchanges while preserving critical information. This enables sessions that run for hours without manual context management—a significant advantage for the collaborative coding workflow Claude is optimized for.

GPT-5.3 Codex: Self-Bootstrapping + Token Efficiency

Codex 5.3’s technical story centers on two innovations: the model participated in its own development, and it achieves frontier results with fewer tokens.

Self-Bootstrapping Development

OpenAI describes GPT-5.3 Codex as “the first model that was instrumental in creating itself.” Early versions were used to debug training runs, manage deployment infrastructure, and diagnose evaluation results. This isn’t recursive self-improvement in the sci-fi sense—it’s a model accelerating the development feedback loop by handling infrastructure work that previously bottlenecked human researchers.

The model was co-designed for and served on NVIDIA GB200 NVL72 systems, achieving its 25% inference speed improvement through combined model and hardware optimization.

Token-Efficient Patching

GPT-5.3 Codex achieves its SWE-Bench Pro results with fewer output tokens than any prior model. This is a direct cost improvement: better accuracy per token means lower cost per patch. The model also fixed three specific failure patterns from its predecessor:

• Non-deterministic linting loops (model would endlessly retry failed lints)
• Insufficient bug-analysis evidence (premature fix attempts without diagnosis)
• Premature completion in flaky-test scenarios (declaring success on intermittent passes)

Head-to-Head Benchmarks

Here’s where the three-way split becomes concrete:

Bold = category leader. The pattern is clear: Google leads reasoning and scientific benchmarks, OpenAI leads terminal execution, Anthropic leads real-world expert preference.

How They Actually Behave Differently

Benchmarks measure capability. Behavior determines whether engineers prefer a model. Based on reports from Interconnects, DataCamp, and practitioner testing, there are clear behavioral differences.

Opus 4.6: The Collaborative Pair Programmer

Claude tends to ask clarifying questions before acting. It will surface assumptions, propose alternatives, and seek confirmation on ambiguous requirements. This is intentional—Opus is optimized for sessions where the developer is actively steering.

Developers report trusting Claude to “understand the context of a fix and generally get it right” on broad horizontal tasks like git operations, data analysis, PR management, and refactoring. It handles multiple-instruction queues without detailed babysitting.

Codex 5.3: The Autonomous Executor

Codex tends to implement first and ask questions later. It’s more assertive, faster to produce output, and optimized for well-scoped, clear problems. The tradeoff: it “can skip files, put stuff in weird places” on broad tasks and requires “babysitting in terms of more detailed descriptions when doing somewhat mundane tasks,” as the Interconnects review noted.

Where Codex excels is terminal work—shell environments, deployment pipelines, and infrastructure automation. The 77.3% Terminal-Bench score reflects a model fine-tuned for autonomous execution in constrained environments.

Gemini 3.1 Pro: The Reasoning Specialist

Gemini 3.1 Pro’s Deep Think mode trades speed for thoroughness. It is strongest on tasks that require multi-step logical deduction: debugging complex race conditions, analyzing mathematical proofs, or handling science-heavy codebases. The MEDIUM thinking level introduces a practical middle ground—less latency than full Deep Think, more thorough than standard inference.

Developers on community forums note Gemini’s particular strength in frontend and UI work—turning Figma-like mockups into code, implementing hover states, and aligning layouts based on visual specs. The native multimodal architecture gives it an edge when the problem involves visual reasoning alongside code generation.

The Right Mental Model for February 2026

The leaderboard is now genuinely split three ways. Rather than asking “which model is best,” the productive question is which architectural bet maps to your workload:

Some developers are settling into multi-model workflows—Claude for architecture and complex implementation, Gemini for prototyping and long-context work, Codex for deployment and long-running autonomous tasks. Others prefer sticking with one model because behavioral consistency matters more than marginal benchmark advantages.

What Engineers Are Saying

Real-world feedback from vetted sources paints a more nuanced picture than benchmarks alone.

On Gemini 3.1 Pro, Vladislav Tankov (Director of AI at JetBrains) reported a “15% quality improvement over previous versions,” noting the model is “stronger, faster… and more efficient, requiring fewer output tokens.” Hanlin Tang (CTO of Databricks) stated it achieved “best-in-class results” on OfficeQA, a benchmark for grounded reasoning across tabular and unstructured data. Andrew Carr (Co-founder at Cartwheel) highlighted “substantially improved understanding of 3D transformations,” noting it resolved long-standing rotation order bugs in 3D animation pipelines.

On Opus 4.6, Codecademy’s review emphasized the long-context retrieval jump from 18.5% to 76% as the standout practical improvement. Boris Cherny (Claude Code developer at Anthropic) summarized it as “more agentic, more intelligent, runs for longer, and is more careful and exhaustive.” The Interconnects review found Opus handles broad horizontal tasks reliably without requiring detailed instructions—a significant UX advantage for day-to-day engineering work.

On Codex 5.3, the same Interconnects review noted that while Codex has “the best top-end ability in software understanding,” switching from Opus to Codex “feels like needing to babysit the model.” DataCamp’s analysis highlighted the real-time interactive collaboration as a meaningful UX shift—you can steer Codex mid-task rather than waiting for final output, enabled via Settings > General > Follow-up behavior.

The emerging consensus: benchmark leadership doesn’t translate directly to developer preference. Opus 4.6 has the highest satisfaction in collaborative workflows. Codex 5.3 is preferred for constrained, autonomous long running tasks. Gemini 3.1 Pro offers the best reasoning-to-dollar ratio and is earning rapid adoption for cost-sensitive, reasoning-heavy workloads. The right choice depends on how you work, not which number is highest.

References

• Google Blog: Gemini 3.1 Pro
• VentureBeat: Google Launches Gemini 3.1 Pro
• Interconnects: Opus 4.6 vs Codex 5.3
• Codecademy: Anthropic Claude Opus 4.6
• DataCamp: GPT-5.3 Codex
• Menlo Ventures: The State of Generative AI in the Enterprise
• OfficeChai: Gemini 3.1 Pro Benchmarks