What’s Inside GPT‑6’s ‘Spud’ Release? 5‑6 Trillion Parameters and 2 M Token Context

Release Overview

On 2026‑04‑14 OpenAI released GPT‑6, codenamed “Spud”, after an 18‑month effort costing over $20 billion and using roughly 100 000 H100 GPUs. OpenAI’s internal evaluation places the model at 70‑80% of its AGI completion metric.

Technical Changes

1. MoE Sparse Architecture

GPT‑6 contains 5‑6 trillion parameters, more than three times GPT‑5.4’s 1.8 trillion. The Mixture‑of‑Experts design activates only about 10% of parameters (≈500‑600 billion) during inference, resulting in sub‑linear compute growth, roughly 40% lower energy consumption compared with dense models of similar size, and unchanged response latency.

2. Symphony Full‑Modal Architecture

Text, image, audio, and video are encoded into a single vector space, eliminating cross‑module signal loss. Demonstrated effects include markedly higher accuracy on complex form analysis, extraction of decision points from meeting recordings, and direct generation of front‑end code from hand‑drawn sketches.

3. Dual‑System Reasoning

System‑1 delivers fast, intuitive replies for routine dialogue. System‑2 performs slower logical verification for multi‑step reasoning, error correction, and self‑healing. OpenAI reports a hallucination rate below 0.1% (unverified) and a math‑reasoning accuracy of 92.5%, a 47% improvement over GPT‑5.4. Code‑generation pass rate is claimed at 96.8% (SWE‑bench pending independent verification). Complex code‑refactoring reportedly reduces bugs by about 60%.

4. 2 Million‑Token Context Window

Implemented with hierarchical sparse attention and a rolling memory cache, the window equals roughly 1.5 million Chinese characters. This enables single‑turn processing of medium‑size codebases, full legal contracts, annual reports, or 100‑page technical documents with >95% accuracy. The capacity also makes many RAG pipelines obsolete: a knowledge base under 2 M tokens can be fed directly without retrieval.

Benchmark Comparison

SWE‑bench Verified (code bug fixing): GPT‑6 ~90%+, Claude Opus 4.7 87.6%, GPT‑5.5 ~82%, GPT‑5.4 ~80%.

SWE‑bench Pro: GPT‑6 ~70%+, Claude Opus 4.7 64.3%, GPT‑5.5 ~60%, GPT‑5.4 57.7%.

GPQA Diamond (graduate‑level reasoning): GPT‑6 ~96%+, Claude Opus 4.7 94.2%, GPT‑5.4 94.4%.

MMMLU: GPT‑6 ~94%, Claude Opus 4.7 91.5%, GPT‑5.4 ~92%.

MCP‑Atlas (tool calling): GPT‑6 ~80%, Claude Opus 4.7 77.3%, GPT‑5.5 ~70%, GPT‑5.4 68.1%.

OSWorld (desktop automation): Claude Opus 4.7 78.0% (GPT‑6 not reported).

Terminal‑Bench 2.0 (terminal tasks): GPT‑6 ~78%, Claude Opus 4.7 69.4%, GPT‑5.5 ~72%, GPT‑5.4 75.1%.

Engineering Selection Guidance

Production‑grade multi‑file code refactoring → Claude Opus 4.7 (≈95% functional correctness, better cross‑file consistency).

Ultra‑large codebase analysis → GPT‑6 (2 M token capacity).

Rapid prototyping & heavy terminal use → GPT‑5.5 (fast response, token efficiency).

Mathematical proof / deep reasoning → GPT‑6 (claimed 47% math accuracy boost, pending verification).

Desktop automation / GUI tasks → Claude Opus 4.7 (OSWorld 78.0%).

Cost‑sensitive / high‑throughput workloads → GPT‑5.5 or Gemini 3.1 Pro (best price‑performance).

Full‑document contract analysis → GPT‑6 (2 M token + long‑context retrieval).

Agent Capabilities

Integration of ChatGPT, Codex, and the Atlas browser allows autonomous execution: fetching web data, generating documents, and sending emails without user intervention. OpenAI reports a 75% success rate on complex tasks and a three‑fold efficiency gain.

API calls remain backward compatible; only the model name changes.

Python SDK provides migration examples, minimizing integration effort.

Details of the persistent memory mechanism are not fully disclosed.

Pricing

GPT‑5.4: $2.5 / M input tokens, $12 / M output tokens, context ~1 M tokens.

GPT‑6: same $2.5 / M input and $12 / M output pricing, context 2 M tokens.

Claude Opus 4.7: $5 / M input, $25 / M output, context 1 M tokens.

GPT‑5.5: ~ $2.5 / M input, ~ $15 / M output, context ~1 M tokens.

Memory System

Three long‑term layers:

Cross‑session memory retains user preferences, ongoing projects, and communication style.

Personalized persona adapts tone and corporate branding.

Implicit preference inference tags language or tool preferences with confidence scores.

In a 50‑turn dialogue test GPT‑6 remembered the first turn perfectly, a limitation observed in GPT‑5.4 on 50‑page documents.

Competitive Landscape

Context window: GPT‑6 2 M tokens; Claude Opus 4.7 and DeepSeek V4 1 M tokens; Kimi K2.6 not disclosed.

Multimodal: GPT‑6 uses native unified Symphony; others use separated pipelines.

Code ability: GPT‑6 claims strongest but unverified; Claude Opus 4.7 currently verified strongest; DeepSeek V4 strong; Kimi K2.6 leads open‑source rankings.

Price: GPT‑6 $2.5/$12; Claude Opus 4.7 $5/$25; DeepSeek V4 low in China; Kimi free/low.

Domestic access: GPT‑6 and Claude Opus 4.7 unavailable; DeepSeek V4 and Kimi available.

Open‑source: only DeepSeek V4 (Pro) and Kimi (open) provide source code.

Open Questions

How the 0.1% hallucination rate was measured (test set and methodology).

Which tasks trigger System‑2 reasoning and the associated latency overhead.

Privacy, deletion, and compliance policies for the persistent memory.

Timeline for independent verification of GPT‑6’s claimed capabilities.

Conclusion

GPT‑6 doubles the context window, introduces a unified multimodal Symphony architecture, and adds autonomous agent execution, making many retrieval‑augmented pipelines obsolete. While GPT‑6 presents the highest headline capabilities, practical model selection should consider availability, verified performance, and cost.