GPT-5.4 vs Claude Opus 4.8 vs Gemini 3.5: Real Cost Comparison

Which of the big three is actually cheapest in 2026? It depends entirely on your use case — and the gaps are smaller than the marketing suggests. This guide breaks down the current pricing for OpenAI's GPT-5.4, Anthropic's Claude Opus 4.8, and Google's Gemini 3.5 Flash, with real-world cost math you can apply to your own workloads. All numbers are pulled from the live Tokenia model catalog of 100+ models.

The Core Pricing Table (2026)

Model	Input (per 1M)	Output (per 1M)	Context	Best For
GPT-5.4	$2.50	$15.00	400K	Balanced agentic workflows, tool use, broad ecosystem
Claude Opus 4.8	$5.00	$25.00	200K	Hardest reasoning, long sustained quality, prompt caching
Gemini 3.5 Flash	$1.50	$9.00	1M	High-volume work, vision, the largest context window
GPT-5 nano	$0.05	$0.40	400K	Classification, routing, simple extraction at scale
Claude Haiku 4.5	$0.80	$4.00	200K	Summarization, formatting, mid-tier tasks

Real-World Cost: 1M Requests/Month

Take a common scenario: a workload that processes 1 million requests per month. Each request averages 500 input tokens (including the system prompt) and generates 300 output tokens.

# Monthly token totals:
input_tokens  = 1,000,000 × 500  = 500M
output_tokens = 1,000,000 × 300  = 300M

# GPT-5.4  ($2.50 / $15.00):
cost = (500 × $2.50) + (300 × $15.00)  = $1,250 + $4,500 = $5,750/mo

# Claude Opus 4.8  ($5.00 / $25.00):
cost = (500 × $5.00) + (300 × $25.00)  = $2,500 + $7,500 = $10,000/mo

# Gemini 3.5 Flash  ($1.50 / $9.00):
cost = (500 × $1.50) + (300 × $9.00)   = $750 + $2,700  = $3,450/mo

On this workload, Gemini 3.5 Flash comes in cheapest at $3,450/mo — about 40% less than GPT-5.4 and 66% less than Opus 4.8. Note how different this is from a year ago: the "flash" tier is no longer 16× cheaper than the frontier. The premium for Opus 4.8 is real, but you're paying it for the hardest reasoning, not for routine work.

If you truly just need cheap tokens, none of these three is the answer — an open model like DeepSeek V4 Flash ($0.09/$0.18) is an order of magnitude cheaper still. See our DeepSeek vs GPT breakdown for when that trade-off makes sense.

GPT-5.4: The Balanced Default

GPT-5.4 sits in the sweet spot of price, capability, and ecosystem maturity in 2026.

• Tool use & agents: The most reliable function-calling and multi-tool orchestration of the three.
• Ecosystem: Every major framework (LangChain, LlamaIndex, the Agents SDK) targets it first.
• Structured outputs: Strict JSON-schema enforcement is rock-solid.
• 400K context + web search: Enough headroom for most RAG and agent loops.

Best for: agentic pipelines, tool-heavy apps, and teams that want one dependable default model.

Claude Opus 4.8: The Reasoning Premium

At $5/$25 per 1M, Opus 4.8 is the most expensive of the three — and it earns it on the hardest problems.

• Sustained reasoning: The most consistent quality on long, multi-step problems.
• Prompt caching: Cached input drops to roughly 10% of list price — decisive for long, repeated system prompts.
• Code quality: Clean, well-documented output with fewer bugs.
• Calibrated: Better at saying "I'm not sure" instead of hallucinating a tool call.

Prompt Caching Changes the Math

With Anthropic's prompt caching, repeated input drops to about $0.50/1M (vs $5.00). For a 4,000-token system prompt served 100K times/day:

# Without caching:
4,000 × 100,000 × $5.00/1M  = $2,000/day

# With caching (cache reads ≈ 10% of input):
≈ $200/day  →  saves ~$1,800/day = ~$54,000/month

If your prompts share a large fixed prefix, caching can make Opus 4.8 cheaper in practice than a "cheaper" model without caching.

Gemini 3.5 Flash: Volume & Context Leader

Gemini 3.5 Flash is the cost-and-context leader of this trio.

• $1.50/$9.00 — cheapest of the three, with strong throughput.
• 1M-token context — the largest here, ideal for huge documents and long RAG contexts.
• Native vision and competitive multilingual quality.

Best for: high-volume extraction/classification/summarization, vision, and anything that needs a very large context window. Watch-outs: slightly less predictable on deeply nested instructions than Opus 4.8.

Recommendations by Task

Task	Pick	Why
High-volume support / extraction	Gemini 3.5 Flash	Cheapest of the three; quality is sufficient
Agentic / tool-use workflows	GPT-5.4	Best function-calling reliability
Hardest reasoning / analysis	Claude Opus 4.8	Most consistent on multi-step problems
Long documents (>200K tokens)	Gemini 3.5 Flash	1M context fits what others can't
Classification / routing at scale	GPT-5 nano	Overkill to use a frontier model
Long, repeated system prompts	Claude Opus 4.8	Prompt caching flips the cost math
Rock-bottom cost	DeepSeek V4 Flash	~10× cheaper than this trio

The Hybrid Strategy

The best teams don't pick one model — they route by task. A typical mixed-workload split for the scenario above:

# 70% simple  → Gemini 3.5 Flash:  0.70 × $3,450 = $2,415
# 20% medium  → GPT-5.4:           0.20 × $5,750 = $1,150
# 10% hardest → Claude Opus 4.8:   0.10 × $10,000 = $1,000
# Total:                                          $4,565/mo

# vs. sending everything to Opus 4.8: $10,000/mo
# Savings: ~54%

Track Your Real Costs

Before you commit, measure what your actual prompts cost. Paste any prompt into Tokenia to see token counts and dollar costs across all three — and 100+ other models — at once. Use the built-in 3-way comparison to put GPT-5.4, Opus 4.8, and Gemini 3.5 side by side.