GPT-5.5 Outpaces Opus 4.7 in Speed and Token Efficiency
In four one-shot coding experiments, GPT-5.5 took half the time (21 min vs 41 min total), used 70% fewer output tokens (70k vs 250k), and cost $3 less overall, despite doubled per-token pricing.
Efficiency Claims Hold Up: Fewer Tokens, Faster Outputs
OpenAI positions GPT-5.5 as delivering higher quality with fewer tokens and less handholding, targeting enterprise tasks like autonomous decomposition—handling vague prompts by self-identifying ambiguities and executing steps independently. Benchmarks support this: Terminal Bench 2.0 score of 82.7 beats GPT-5.4's 75.1 and Opus 4.7's 69.4; it leads GDP val (knowledge work), Frontier Math, and Cyber Gym over Opus 4.7 and Gemini 3.1 Pro, though Opus retains SweetBench Pro for GitHub issue resolution. Internal evals show GPT-5.5 using fewer output tokens (costlier than inputs) for same or better results vs GPT-4. Pricing doubled from GPT-5.4 ($2.50/$15 per 1M in/out) to $5/$30, slightly above Opus ($5/$25 out), but token savings offset this—output efficiency drives real costs down for production use.
Track your own metrics via JSON logs in tools like Codex or Claude Code: query start/end times, input/output tokens, and requests to compute costs accurately. This reveals GPT-5.5's edge in runtime (e.g., 4x faster on some tasks) and output tokens, enabling cheaper scaling for agentic workflows with tool calling, multi-agent execution, and reusable setups.
One-Shot Coding Showdown: GPT-5.5 Wins on Metrics, Mixed on Polish
Four identical one-shot prompts tested personal brand sites, solar system sims, 3D space shooters, and ecosystem evolutions—no iterations, raw model output in Codex (GPT-5.5, 400k context) vs Claude Code (Opus 4.7, 1M context).
- Personal site: GPT built interactive elements (context maps, consoles) in 4 min vs Opus's 14 min; $1 vs $5 cost; GPT used fewer tokens overall.
- Solar sim: Opus edged visually (better aspect ratios, glows) and cost $1 less, finishing 1 min slower.
- Space shooter: GPT's smoother physics/controls won playability (WASD/move, shift/boost, space/shoot); half the time, under $3 vs $4.50.
- Ecosystem sim: Both buggy (stuck populations, unresponsive controls), but GPT used 28k output tokens vs Opus's 100k+ despite double inputs.
Aggregated: GPT halved total runtime (20:49 vs 40:43), matched inputs (~2.6M tokens), slashed outputs (70k vs 250k), saved $3 total. Use this for agent coding: GPT accelerates prototypes, but Opus shines on visuals/complexity where token savings matter less than output quality.
Builder Takeaways: Test Use Cases, Not Benchmarks
Switching from GPT-5.4? Recalculate unit economics—doubled price but 70% output savings compound at scale. Anthropic leads real-world SWE (SweetBench), so benchmark your tasks: vague prompts reveal autonomous decomposition (GPT strength). OpenAI's ecosystem (Codex, ChatGPT, Atlas) locks in platform value over standalone models. Fast releases (6 weeks from 5.4) make model-specific content obsolete quickly—focus on use-case experiments over leaderboards. Run one-shots in your harness, log tokens/time/cost, and pick per task: no universal winner.