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Is GPT-5.4 Better Than Claude Opus 4.6?
They're close — within ~5% on most benchmarks. Neither is a clear winner. The right answer depends entirely on what you're doing.
GPT-5.4 Wins
- General reasoning & breadth
- SWE-Bench Pro: 57.7% vs Opus ~45%
- Native computer-use
- Cost — half the price of Opus
Opus Wins
- Abstract/deep reasoning: +16 pts on ARC-AGI-2
- Large codebase navigation
- Extended thinking quality
- Sustained multi-step agentic sessions
Token Costs
| Model | Input (per MTok) | Output (per MTok) | Tier |
|---|---|---|---|
| GPT-5.4 Standard | $2.50 | $15 | Best Value |
| GPT-5.4 Pro Reasoning | $30 | $180 | Premium |
| Claude Opus 4.6 | $5 | $25 | Flagship |
| Claude Sonnet 4.x | $3 | $15 | Balanced |
GPT-5.4 is the better value for most tasks. Opus is the better thinker for the hardest tasks. If cost matters and you're not doing deep abstract reasoning — GPT-5.4 wins.
Codex Spark — What Is It?
A speed-optimized coding model running at 1,000+ tokens/sec via Cerebras hardware. Built on GPT-5.3 (not 5.4).
⚠ Important Clarifications
- NOT more powerful than Opus — trades depth for speed
- Coding only — can't do analysis, research, or conversation
- CANNOT be used as a main agent — too narrow
- ChatGPT Pro only ($200/mo) — limited preview, no public API yet
Clear Sonnet vs Opus Replacement
Replacing Sonnet (Main Daily Agent)
Option: GPT-5.4 via Codex OAuth
- ChatGPT Plus — $20/mo flat
- ChatGPT Pro — $200/mo flat (includes Spark)
- Comparable performance to Sonnet for everyday tasks
- No per-token API billing
Replacing Opus (Deep Analysis / Dwight)
Option A — Keep Opus
- Anthropic API key, selective use
- ~$30–80/mo depending on volume
- Full quality — no compromise
- Best for Dwight's deepest tasks
Option B — Switch to GPT-5.4
- Included in Pro OAuth flat rate
- Accept ~15–20% quality loss
- Works for 80% of analysis tasks
- Saves significant API spend
ChatGPT Pro Thresholds vs Anthropic
Current Anthropic Tier 1 Limits
Anthropic API Limits
- 40,000 tokens/min
- 50 req/min
- 1M tokens/day
ChatGPT Pro Message Windows
ChatGPT Pro OAuth Limits
- 198–1,008 GPT-5.4 messages per 5-hour window
- Window resets every 5 hours (~4–5 windows/day)
- Translates to roughly 800–5,000+ messages/day
- Sub-agents all count against the same OAuth pool
⚠ Parallel Agent Risk
- Heavy days with 5+ agents in parallel could push limits
- Comparable to Anthropic on normal workdays
- Pro is 6x Plus — but not unlimited
- Plan for burst capacity on heavy dispatch days
On normal days: ChatGPT Pro limits are comfortable. On heavy days (many parallel sub-agents, large context windows): monitor the window and stagger dispatches if needed.
Gemma 4 — Sonnet or Opus Tier?
Hardware Requirements
| Model | Hardware Needed | Status |
|---|---|---|
| Gemma 4 9B | Current 16GB Mac Mini | Slower |
| Gemma 4 27B | 32GB RAM Mac Mini (upgrade needed) | Needs Upgrade |
| Full Opus-competitive Gemma 4 | Mac Studio / Mac Pro level | Major Upgrade |
Timeline to Local Capability
Sonnet-class performance locally on current Mac Mini (with Gemma 4 + quantization advances)
Opus-class performance locally — requires TurboQuant advances AND better hardware
TurboQuant / Local Model Breakthrough
This is real technology — not vaporware. Quantization advances are enabling 2–4 bit precision with minimal quality loss compared to full-precision models.
✓ What's Real
- 2–4 bit precision quantization without major quality degradation
- Moving fast — field is advancing week over week
- Enables running larger models on consumer hardware
Sonnet-class models locally on current 16GB Mac Mini — within reach with TurboQuant applied to Gemma 4 or similar
Opus-class locally — still requires both quantization advances AND hardware upgrades. Not happening on current Mac Mini regardless of quant.
Don't buy Mac Studio to run local Opus today. Wait 6 months. The model landscape is moving fast enough that the calculus changes significantly in the near term.
What Do You Actually Lose Without Opus?
You DO Lose
- Very deep multi-step research chains
- Complex strategic analysis where nuance matters
- Large codebase architecture reasoning
- Subtle inference on ambiguous data
You DON'T Lose
- Everyday research
- Most analysis tasks
- Tool use & delegation
- Conversation & summarization
- Coding tasks
- Standard agentic workflows
Real Example: When Opus Earns Its Keep
The doTERRA org analysis — inferring Jensen's role from indirect signals and organizational patterns. Opus makes nuanced inferences GPT-5.4 misses or softens. GPT-5.4 gets ~80% of the way there with slightly less inferential depth. That 20% delta is exactly where Opus justifies the price premium.
If the task needs "read between the lines at scale" or "reason across a huge codebase with competing constraints" — that's Opus territory. Everything else is fair game for GPT-5.4.
Can You Keep Same Workflow Without Huge API Bills?
ChatGPT Pro, flat, no API bills
- Main agent: GPT-5.4 via OAuth
- Cody/Bomb: GPT-5.4 + Spark via OAuth
- Scout, Drew, Clips: GPT-5.4 via OAuth
- Dwight: GPT-5.4 via OAuth
- Accept ~15% quality loss on deepest reasoning
- Zero API overage risk
Pro OAuth + selective Opus API
- All agents: GPT-5.4 via Pro OAuth
- Dwight (max depth tasks): Opus API key
- ~$30–80/mo extra for Opus on demand
- Full quality on Dwight's hardest tasks
- No quality compromise on critical analysis
Path B is the sweet spot. $230–280/mo keeps Opus available for Dwight when it actually matters, while cutting the overall bill by 5–6x. Path A is the right move if cash flow is the primary concern and you can accept the Dwight quality tradeoff.