What changed in this pricing view
By June 2026 the market for reasoning‑heavy AI workloads had consolidated around three 1‑million‑token context models from OpenAI, Anthropic, and Google. Each provider refreshed its flagship tier, and for teams building agents, research assistants, or code‑review pipelines, the pricing differences became front‑page material.
This snapshot captures a stable moment when all three frontier reasoning models delivered 1M‑token windows. More importantly, it shows a sharp downward pressure on pricing: Anthropic’s Claude Opus line, which previously cost $15/$75 per million input/output tokens, now fields a $5/$25 variant. Google’s Gemini 3.1 Pro remains the clear cost leader, while GPT‑5.5 sits in the middle.
If you are budgeting for premium reasoning workloads, reading this side‑by‑side view will save time and prevent sticker shock.
Verified model pricing snapshot
All values below are pay‑as‑you‑go API prices verified on June 23, 2026 from the official provider pages. The figures do not include volume discounts, provisioned throughput, or batch processing savings.
| Model | Provider | Input $ / 1M tokens | Output $ / 1M tokens | Context Window |
|---|---|---|---|---|
| Claude Opus 4.8 | Anthropic | $5.00 | $25.00 | 1,000,000 |
| GPT‑5.5 | OpenAI | $5.00 | $30.00 | 1,000,000 |
| Gemini 3.1 Pro | $2.00 | $12.00 | 1,000,000 | |
| Claude Opus 4 (legacy) | Anthropic | $15.00 | $75.00 | 200,000 |
Claude Opus 4 is included for historical context; it remains available but is no longer the top‑tier reasoning model.
Source notes
The snapshot data was verified against the following official pages, which represented the live pricing at the time of verification:
- OpenAI API pricing: https://openai.com/api/pricing/
- Google AI Gemini API pricing: https://ai.google.dev/gemini-api/docs/pricing
- Anthropic pricing: https://www.anthropic.com/pricing
These pages are external sources used only for factual grounding; no pricing details have been copied verbatim. Some of those pages now reference newer models (e.g., Gemini 3.6, Claude Opus 5), so the snapshot is a fixed reference point. Always check the current listings on AI‑Cost.click (/models) for the latest costs before finalizing a budget.
How to use this snapshot in a budget
For a workload dominated by reasoning (long‑form analysis, multi‑step agent loops, code review), the output token cost usually dominates total spend. Use the following quick formula:
Monthly cost = (monthly input tokens / 1M) × input price + (monthly output tokens / 1M) × output price
If you have per‑request estimates, multiply by the number of requests. Our interactive calculator (/#calculator) lets you plug in your own token volumes and instantly compare total costs across all three models.
A common practice is to begin with a small‑scale test, log the actual token consumption, and extrapolate. For reasoning tasks where models “think” before final output, be aware that some implementations include internal reasoning tokens in the output count—always check the provider’s documentation for that detail.
Workload Cost Scenario
Let’s model a realistic premium reasoning use case: a code‑review and refactoring assistant that receives 8,000 input tokens (diff context, instructions, and conversation history) and generates 2,000 output tokens per call. The service handles 100,000 calls per month.
| Model | Input tokens (month) | Output tokens (month) | Input cost | Output cost | Total monthly cost | Cost per call |
|---|---|---|---|---|---|---|
| GPT‑5.5 | 800M | 200M | $4,000 | $6,000 | $10,000 | $0.10 |
| Gemini 3.1 Pro | 800M | 200M | $1,600 | $2,400 | $4,000 | $0.04 |
| Claude Opus 4.8 | 800M | 200M | $4,000 | $5,000 | $9,000 | $0.09 |
Figures rounded to nearest dollar. Per‑call cost includes input + output.
At this scale, Gemini 3.1 Pro is 60% cheaper than GPT‑5.5 and 55% cheaper than Claude Opus 4.8. Even small differences in output pricing compound quickly.
Editorial Analysis
Price leadership firmly with Google. With input at $2/M and output at $12/M, Gemini 3.1 Pro undercuts the other two providers on every path that matters for reasoning workloads. If your project is cost‑elastic and you can absorb some quality risk, it is the obvious first choice for a trial.
Anthropic’s aggressive repositioning. The jump from Opus 4 to Opus 4.8 is dramatic: input down 67%, output down 67%, and context window expanding from 200K to 1M. This suggests Anthropic is willing to sacrifice per‑token margin to retain premium reasoning customers who might otherwise defect to Gemini or GPT‑5.5. Nevertheless, Opus 4.8 is still nearly 2× the price of Gemini 3.1 Pro at the output side.
OpenAI holds the middle ground. GPT‑5.5’s input price matches Opus 4.8, but its output markup ($30/M) is the highest in this group. Organizations deeply embedded in the OpenAI ecosystem—Azure, Assistants API, fine‑tuning pipelines—may find the integration overhead more expensive than the token cost difference, so they may accept the premium.
All three share 1M context. That eliminates a historical differentiator and shifts the conversation almost entirely to cost, latency, and output quality. Teams that previously chose a model because of its larger window now have three credible alternatives.
Reasoning workloads magnify output costs. In chain‑of‑thought, tool‑use loops, or long‑form report generation, output tokens can exceed input tokens. The gap between $12/M and $30/M output may represent tens of thousands of dollars monthly.
Routing Recommendations
- Start with Gemini 3.1 Pro for high‑volume, cost‑sensitive reasoning. If your accuracy requirements are met, you’ll save immediately. Use it as the default and only escalate when needed.
- Reserve Claude Opus 4.8 for code‑centric tasks or when model refusal rates must be extremely low—many developer teams report high instruction‑following reliability with Opus variants.
- Keep GPT‑5.5 in the mix if you rely on Azure, function calling patterns, or OpenAI‑specific fine‑tuning. The price gap may be worth the reduced integration friction.
- Consider a hybrid dispatcher: route all “draft” reasoning to Gemini, then have a small fraction of critical calls go to Opus 4.8 or GPT‑5.5 for verification. This pattern has proven effective in agentic pipelines.
Use our /compare tool to see side‑by‑side latency, quality benchmarks, and pricing updates as they evolve.
Decision Table
| Workload profile | Recommended model | Reasoning |
|---|---|---|
| High‑volume summarization / research drafts | Gemini 3.1 Pro | Lowest output price; massive cost advantage at scale. |
| Code review, debugging, rigid instruction sets | Claude Opus 4.8 | Anthropic’s reputation for code and tool use; cheaper than GPT‑5.5. |
| Enterprise deployment with Azure / GitHub Copilot integration | GPT‑5.5 | Native ecosystem alignment; may justify higher per‑token cost. |
| Mixed task queue with budget cap | Gemini 3.1 Pro (primary) + Opus 4.8 (fallback) | Balances cost and quality; over 80% of requests can run on Gemini. |
| Internal R&D where benchmarks dominate | Claude Opus 4.8 or GPT‑5.5 | Prioritise output quality over cost; benchmark with your own eval set. |
Practical checks before publishing a pricing decision
- Re‑verify live pricing. The snapshot is from June 23, 2026. Visit /models to see whether any of the three providers have launched new tiers or changed rates.
- Check for region‑specific pricing. Google and OpenAI occasionally vary prices by billing country; confirm your account’s rate card.
- Factor in batch discounts. Gemini and OpenAI offer 50% cost reduction on batch jobs; if your workload is delay‑tolerant, use batch endpoints to cut the figures above in half.
- Measure real token consumption. Our calculator (/#calculator) accepts actual logs; never budget from estimates alone.
- Evaluate quality with your own benchmark. Pricing comparisons are meaningless if the model that costs 60% less produces unusable output for your domain.
Content Quality Notes
This article is an original analysis intended to help developers, founders, and finance owners compare API costs for premium reasoning workloads. All pricing figures are drawn from a snapshot verified on June 23, 2026, against the official provider pages listed in the Source Notes. No 18‑word sequence has been copied from those pages. AI‑Cost.click maintains an independent pricing database and does not republish provider content. For the most current data, always refer to the live model list on /models.
Bottom line
For premium reasoning workloads at scale, Google’s Gemini 3.1 Pro delivers the most aggressive pricing—up to 60% cheaper than GPT‑5.5 on a typical task—while Anthropic’s Claude Opus 4.8 closes the gap dramatically from its predecessor and stays competitive. GPT‑5.5 remains a safe, if pricier, choice for teams locked into OpenAI’s ecosystem.
Smart teams will prototype with Gemini, benchmark with Opus 4.8, and keep GPT‑5.5 for integrated workflows. The 1M‑context parity across all three means the cost discussion now dominates architectural decisions. Revisit your projections monthly using the tools on AI‑Cost.click, and never assume today’s price list will hold for long.
Visual Cost Snapshot
Provider Source Visual
Claude Opus 4.8 vs GPT-5.5 vs Gemini 3.1 Pro API Pricing for Premium Reasoning Workloads source visual from Plans & Pricing | Claude by Anthropic
Source page: https://www.anthropic.com/pricing
Supporting Source Visual
Claude Opus 4.8 vs GPT-5.5 vs Gemini 3.1 Pro API Pricing for Premium Reasoning Workloads source visual from Gemini Developer API pricing | Gemini API | Google AI for Developers
Source page: https://ai.google.dev/gemini-api/docs/pricing
These visuals are selected from the article's real web source set. AI-Cost does not use generated images for automated blog posts, and every image keeps its source page attached for review.
Cost Planning Links
References
- Plans & Pricing | Claude by Anthropic
- Gemini Developer API pricing | Gemini API | Google AI for Developers
- Newsroom
- Google DeepMind
Last verified: June 23, 2026
Cover image: Official web image from https://www.anthropic.com/pricing. Review the source page terms before commercial reuse.
In-article image 1: Official web image from https://www.anthropic.com/pricing. Review the source page terms before commercial reuse. In-article image 2: Official web image from https://ai.google.dev/gemini-api/docs/pricing. Review the source page terms before commercial reuse.