Claude Opus 4.6 vs Gemini 3.1 Pro: Architecture, Context Windows, and Output Dynamics
Three frontier models, three architectural philosophies. Claude leads in output capacity (128K tokens) and raw throughput (107 t/s). Gemini processes 1M tokens across five native modalities at 66 t/s. GPT-5.3-Codex brings a coding-optimized 400K context with 93 t/s throughput in isolated cloud sandboxes. All three deploy adaptive compute systems that fundamentally change how intelligence is allocated per query.
Core Architecture Specifications
↑ Industry-leading [2]
→ Standard capability [5]
↑ Faster generation [7]
→ Lower but cheaper [7]
↑ Coding-optimized [27]
↑ Fastest Codex variant [7]
The Million-Token Context Window
Both Claude Opus 4.6 and Gemini 3.1 Pro support 1 million input tokens, equivalent to approximately 750,000 words or roughly 3,000 pages of text. This shared specification masks significant architectural differences in how each model actually processes this massive context. [2][5]
Google’s approach to context processing leverages its expertise in large-scale information retrieval. Gemini 3.1 Pro processes the full context window natively — including text, images, audio, video, and PDF inputs simultaneously — without requiring the context to be decomposed into homogeneous text chunks. This means a developer can submit a 45-minute video alongside a 200-page technical document and a codebase, and the model processes all three as a unified context. [5]
Anthropic’s approach focuses the context window entirely on text and image inputs, but with demonstrably superior reasoning depth over long contexts. Claude Opus 4.6 shows less performance degradation when critical information is buried deep within very long contexts — a phenomenon known as “needle-in-a-haystack” performance — than earlier models. [2][15]
Output Capacity: The 128K Advantage
Perhaps the most consequential architectural difference is output capacity. Claude Opus 4.6 can generate up to 128,000 output tokens in a single response — approximately 96,000 words — compared to Gemini’s 65,536 token (approximately 49,000 word) limit. [2][5]
This near-2x advantage in maximum output length has profound implications for enterprise workflows. A 128K output window means Claude can generate complete, production-grade documents — entire technical specifications, full legal contract analyses, comprehensive codebase refactors — in a single pass without requiring multi-turn chunking strategies. [2]
For agentic coding, this output advantage means Claude can generate larger, more coherent code changes in a single operation, reducing the number of autonomous action steps needed to complete complex refactoring tasks. Each additional step in an agentic sequence introduces potential for error propagation, so fewer steps means higher end-to-end reliability. [6]
Technical Specifications Side-by-Side
| Specification | Claude Opus 4.6 | Gemini 3.1 Pro | GPT-5.3-Codex |
|---|---|---|---|
| Context Window | 1,000,000 tokens | 1,000,000 tokens | 400,000 tokens |
| Max Output | 128,000 tokens | 65,536 tokens | ~128,000 tokens (est.) |
| Output Throughput | 107 tokens/sec | 66 tokens/sec | 93 tokens/sec |
| Time to First Token | ~1.2s (est.) | ~0.8s (est.) | N/A (async tasks) |
| Input Modalities | Text, Image | Text, Image, Audio, Video, PDF | Text, Image, Code |
| Knowledge Cutoff | March 2025 | March 2025 | March 2025 |
| Grounding/Search | Via MCP tools | Native Google Search | GitHub integration |
| Adaptive Compute | Extended Thinking | Thinking Mode / Effort | o3-optimized reasoning |
| Execution Model | Streaming API | Streaming API | Cloud sandbox (async) |
Throughput and Latency Dynamics
Raw output throughput — the speed at which the model generates tokens — favors Claude at 107 tokens per second versus Gemini’s 66 tokens per second. This 62% throughput advantage means that for output-heavy tasks (code generation, document drafting, analysis reports), Claude delivers results significantly faster. [7]
However, throughput does not tell the complete story. Gemini’s lower throughput is partially offset by lower latency to first token in many scenarios, and its substantially lower per-token cost means that for applications where cost-per-query dominates over time-to-completion, Gemini’s throughput penalty is acceptable. [7]
For real-time applications — chatbots, interactive coding assistants, customer-facing systems — the time-to-first-token metric is often more important than sustained throughput. Neither vendor publishes official TTFT benchmarks, but third-party testing from Artificial Analysis suggests both models deliver sub-2-second first token latency under normal load. [7]
Adaptive Compute: Thinking on Demand
Both models implement sophisticated adaptive compute systems that dynamically adjust the amount of reasoning effort applied to each query. This represents a fundamental shift from the fixed-compute paradigm of earlier models, where every query received the same amount of processing regardless of difficulty. [2][5]
Claude Opus 4.6 features Extended Thinking — the ability to allocate additional reasoning tokens (visible in the API as “thinking” tokens) for complex problems. The API exposes a configurable budget that allows developers to set maximum thinking token allocations, enabling cost-quality tradeoffs at the per-query level. For simple classification tasks, thinking can be minimized. For complex mathematical proofs or multi-step code debugging, the thinking budget can be expanded dramatically. [2]
Gemini 3.1 Pro implements a parallel system called Thinking Mode with discrete effort modifiers. Rather than a continuous budget, Gemini offers preset effort levels (typically low, medium, and high) that adjust the model’s internal compute allocation. This approach is simpler to configure but offers less granular control than Claude’s continuous budget model. [5]
Output Generation Speed (tokens/second)
Implications for Enterprise Architecture
The architectural differences between these models create distinct optimization strategies for enterprise deployments. Organizations running output-heavy workloads — report generation, code synthesis, long-form analysis — should favor Claude’s 128K output window and 107 t/s throughput. These workloads benefit directly from larger single-pass generation and faster delivery. [2][7]
Organizations running input-heavy workloads — document ingestion, media analysis, search-augmented generation across large corpora — may benefit more from Gemini’s native multimodal context processing and lower per-token cost. The ability to natively process video, audio, and PDFs within the context window eliminates preprocessing pipeline complexity. [5]
The adaptive compute systems in both models enable a new deployment pattern: effort-tiered routing. Simple queries can be handled at minimum effort/thinking budget (fast, cheap), while complex queries trigger maximum compute allocation (slower, more expensive, more accurate). This allows a single model deployment to serve both high-volume commodity tasks and low-volume high-complexity tasks efficiently. [2][5]
GPT-5.3-Codex: The Asynchronous Architecture
OpenAI’s GPT-5.3-Codex introduces a fundamentally different architectural approach. Rather than competing on streaming API throughput, Codex operates primarily as an asynchronous cloud agent. Tasks are dispatched to isolated cloud sandboxes — each with its own filesystem, network isolation, and execution environment — where the model works autonomously for minutes to hours before returning results. [27]
The underlying model (codex-1, built on o3) operates with a 400,000-token context window and o3-optimized reasoning chains. Unlike Claude and Gemini’s streaming paradigm where every token is generated in real-time, Codex’s sandbox architecture allows it to execute multi-step workflows — reading code, running tests, iterating on failures — without requiring continuous client connections. [27]
This architectural divergence means direct throughput comparisons are somewhat misleading. GPT-5.2-Codex achieves 93 tokens/second on Artificial Analysis benchmarks, placing it between Claude (107 t/s) and Gemini (66 t/s), but the real performance story is task-level completion time. For complex software engineering tasks, the sandbox model can be more efficient because it eliminates the context-switching overhead of multi-turn conversation loops. [7][27]
“The 128K output window means Claude can generate complete, production-grade documents in a single pass. Each additional step in an agentic sequence introduces error propagation risk — fewer steps means higher end-to-end reliability.”
— Enterprise architecture analysis, February 2026 [2]
Key Takeaways
- Context windows match at 1M tokens: Both models process equivalent input volumes, but Gemini handles more modalities natively.
- Output capacity is the key differentiator: Claude’s 128K output (vs Gemini’s 65K) enables complete document generation in single passes.
- Claude wins on throughput: At 107 vs 66 tokens/second, Claude generates output 62% faster — decisive for output-heavy workloads.
- Adaptive compute changes everything: Both models dynamically allocate reasoning effort, enabling cost-quality optimization at the per-query level.
- The right model depends on workload profile: Output-heavy tasks favor Claude; multimodal input-heavy tasks favor Gemini; autonomous coding tasks favor GPT-5.3-Codex.
- GPT-5.3-Codex redefines execution: Asynchronous cloud sandboxes trade streaming interaction for autonomous multi-step task completion at 93 t/s throughput.
References
- [2] “Introducing Claude Opus 4.6,” Anthropic, February 2026. Available: https://www.anthropic.com/news/claude-opus-4-6
- [5] “Gemini 3.1 Pro: Announcing our latest Gemini AI model,” Google Blog, February 2026. Available: https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-1-pro/
- [6] “Google Antigravity + Claude Code AI Coding Tips,” Reddit r/vibecoding, February 2026. Available: https://www.reddit.com/r/vibecoding/comments/1pevn9n/google_antigravity_claude_code_ai_coding_tips/
- [7] “AI Model Benchmarks + Cost Comparison,” Artificial Analysis, February 2026. Available: https://artificialanalysis.ai/leaderboards/models
- [15] “Gemini vs Claude: A Comprehensive 2026 Comparison,” Voiceflow Blog, February 2026. Available: https://www.voiceflow.com/blog/gemini-vs-claude
- [27] “Introducing GPT-5.3-Codex,” OpenAI, February 2026. Available: https://openai.com/index/introducing-gpt-5-3-codex/
