Gemini 3.1 Pro vs Opus 4.6 vs Codex: Agentic Coding

The Agentic Coding Landscape

February 2026 is the most competitive month in AI coding history. Claude Opus 4.6 launched on February 4, GPT-5.3-Codex on February 5, and Gemini 3.1 Pro on February 19 — three frontier releases in sixteen days. Each model was optimized for a different coding paradigm, and the benchmarks reflect those design decisions clearly.

The three models represent fundamentally different design philosophies. Google optimized Gemini 3.1 Pro for breadth — competitive coding, scientific reasoning, and tool coordination at an aggressive price point. Anthropic focused Opus 4.6 on depth — surgical precision on real-world SWE tasks and expert-level office workflows. OpenAI built GPT-5.3-Codex for speed — terminal execution, sustained agentic loops, and IDE-native coding. For context on how the previous generation compared, see our Claude 4.5 vs GPT-5.2 vs Gemini 3 Pro comparison.

Coding Benchmark Head-to-Head

The coding benchmarks reveal three distinct strengths. Opus 4.6 edges SWE-Bench Verified by 0.2 percentage points over Gemini 3.1 Pro — a near-tie for the most production-relevant coding benchmark. Codex dominates Terminal-Bench 2.0 by a wide margin, while Gemini 3.1 Pro posts the highest LiveCodeBench Pro Elo ever recorded and leads SciCode for scientific coding.

The SWE-Bench Verified near-tie between Gemini 3.1 Pro (80.6%) and Opus 4.6 (80.8%) is the headline result. This is the most production-relevant coding benchmark — it tests real-world bug fixes across open-source Python repositories. A 0.2 percentage point gap is within noise for practical purposes, meaning both models are equally capable for day-to-day SWE tasks.

Where the models diverge is more revealing than where they converge. GPT-5.3-Codex's 77.3% on Terminal-Bench 2.0 is 8.8 points ahead of Gemini and 11.9 points ahead of Opus — a decisive lead on terminal-heavy coding workflows. Gemini 3.1 Pro's 2887 Elo on LiveCodeBench Pro is the highest competitive coding score ever recorded, and its 59% on SciCode (vs Opus's 52%) shows clear strength in scientific programming.

Agentic Task Performance

Agentic benchmarks measure how well models coordinate tools, handle multi-step workflows, and operate autonomously. These tests predict real-world performance in production agent deployments better than pure coding benchmarks.

Gemini 3.1 Pro leads the autonomous agent benchmarks decisively. APEX-Agents, which tests fully autonomous multi-step task execution, shows Gemini at 33.5% vs Opus's 29.8% — a 3.7 percentage point advantage. MCP Atlas, which evaluates tool coordination across many simultaneous tools, shows an even wider gap: 69.2% vs 59.5% for Opus and 61.3% for Sonnet 4.6.

The τ²-bench results tell a different story. On retail scenarios, Opus 4.6 leads at 91.9% — ahead of both Gemini (90.8%) and Sonnet 4.6 (91.7%). On telecom scenarios, Gemini and Opus tie at 99.3%, with both outperforming Sonnet 4.6's 97.9%. These domain-specific agent benchmarks show that Claude models excel at structured customer service workflows, while Gemini excels at open-ended tool coordination.

Reasoning Depth Comparison

Reasoning depth directly affects code quality on novel problems. Models that score higher on abstract reasoning benchmarks consistently produce better solutions for algorithmic challenges, architectural decisions, and edge-case handling. The reasoning benchmarks reveal which model to trust when the problem has no Stack Overflow answer.

Gemini 3.1 Pro dominates pure reasoning. ARC-AGI-2 at 77.1% is 8.3 points ahead of Opus (68.8%) and 24.2 points ahead of GPT-5.2 (52.9%). GPQA Diamond at 94.3% sets a new high-water mark for graduate-level scientific reasoning. HLE without tools shows the same pattern: Gemini leads at 44.4%, followed by Opus at 40.0%.

But when tools enter the picture, Opus 4.6 catches up. On HLE with Search+Code access, Opus leads at 53.1% vs Gemini's 51.4% — suggesting Claude is better at leveraging external tools to augment its reasoning. The GDPval-AA result is even more dramatic: Opus scores 1606 Elo vs Gemini's 1317, a 289-point gap that indicates superior performance on expert-level office and financial tasks. This aligns with Opus's design focus on precision over breadth.

Pricing and Cost Analysis

Pricing is where the three models diverge most dramatically. Gemini 3.1 Pro costs 7.5x less than Opus 4.6 on input tokens, making the cost gap the single largest factor in production architecture decisions. GPT-5.3-Codex uses Codex plan pricing rather than standard per-token rates, creating a different cost model entirely.

To put the cost gap in perspective: processing 1 million input tokens costs $2 with Gemini 3.1 Pro vs $15 with Opus 4.6. For a team running 100M tokens per month through an agentic coding pipeline, that is the difference between $200 and $1,500 in input costs alone. Output costs widen the gap further — $12 vs $75 per million tokens. At scale, Gemini 3.1 Pro's price advantage becomes a decisive architectural factor.

Which Model Should You Choose?

The decision framework is straightforward once you identify your primary workflow. Each model owns a clear niche, and the benchmarks align with real-world use cases.

Building a Multi-Model Strategy

The strongest approach for engineering teams is not choosing one model — it is routing tasks to the model best suited for each workflow. The benchmarks make this routing logic clear: competitive coding and tool coordination go to Gemini 3.1 Pro, production bug-fixing and expert tasks go to Opus 4.6, and terminal-heavy agentic loops go to GPT-5.3-Codex.

Task-Based Routing

// config/model-routing.ts
const MODEL_CONFIG = {
  competitiveCoding: {
    model: "gemini-3.1-pro",
    fallback: "claude-opus-4-6",
    use: "Algorithmic challenges, scientific coding",
  },
  productionSWE: {
    model: "claude-opus-4-6",
    fallback: "gemini-3.1-pro",
    use: "Bug fixes, expert analysis, code review",
  },
  terminalAgentic: {
    model: "gpt-5.3-codex",
    fallback: "gemini-3.1-pro",
    use: "Terminal loops, multi-file refactors",
  },
  toolCoordination: {
    model: "gemini-3.1-pro",
    fallback: "claude-sonnet-4-6",
    use: "MCP tools, multi-service orchestration",
  },
  maxRetries: 3,
  timeoutMs: 120_000,
};

Cost Optimization Strategy

Route the highest-volume tasks to Gemini 3.1 Pro at $2/$12 per million tokens. Reserve Opus 4.6 at $15/$75 for precision-critical tasks where the GDPval-AA and SWE-Bench Verified advantages justify the premium. Use Claude Sonnet 4.6 at $3/$15 as a cost-effective middle tier for tasks that need Claude's style without Opus-level reasoning depth.

Fallback Chains

Build fallback logic for reliability. If Gemini 3.1 Pro is unavailable or rate-limited, fall back to Opus 4.6 for coding tasks or Sonnet 4.6 for cost-sensitive alternatives. If Opus is down, Gemini handles most SWE tasks at near-identical accuracy (80.6% vs 80.8%). If Codex is unavailable, Gemini's 68.5% on Terminal-Bench provides a reasonable fallback. Track accepted patches, reruns, and reviewer edits per model to measure actual engineering throughput and refine routing over time. For broader guidance on web development with AI, our team can help you implement these patterns.

Conclusion

February 2026's three-way frontier model race has produced the most competitive agentic coding landscape in AI history. Gemini 3.1 Pro offers the best breadth-to-cost ratio with leading scores on LiveCodeBench (2887 Elo), ARC-AGI-2 (77.1%), SciCode (59%), and MCP Atlas (69.2%) at $2/$12 per million tokens. Claude Opus 4.6 delivers the highest SWE-Bench Verified score (80.8%) and expert task performance (GDPval-AA: 1606 Elo) for precision-critical production work. GPT-5.3-Codex dominates terminal workflows (77.3% Terminal-Bench) with the fastest agentic inference.

The practical takeaway is clear: no single model wins everywhere, and the best engineering teams will adopt multi-model strategies that route tasks to the model best suited for each workflow. The 7.5x cost difference between Gemini and Opus alone justifies building routing infrastructure for any team running significant AI-assisted coding volume.

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