All three leading model providers now offer native 1‑million‑token context windows at API prices that can swing a monthly bill by thousands of dollars. Choosing one endpoint over another for long‑document Q&A, codebase analysis, or multi‑turn agent sessions isn’t just an engineering decision – it’s a budget decision. In this guide we compare Gemini 3.1 Pro, Claude Opus 4.8, and GPT‑5.5 side by side, using only verified pricing from official pages, and show how to model the real cost of long‑context workloads.
What changed in this pricing view
Since the previous snapshot we recorded, three shifts stand out:
- Claude Opus 4.8 became the current Anthropic flagship and brought the input price down to match GPT‑5.5’s $5/1M, while undercutting it on output ($25 vs $30). The older Claude Opus 4, still listed, remains at $15/$75 with only 200K context – it is now effectively a legacy comparison point.
- Gemini 3.1 Pro continues to offer the most aggressive price across the board, at $2 input and $12 output per 1M tokens, without sacrificing the 1M context window.
- All three flagships now share the 1M context ceiling, making long‑context planning purely about cost and quality tradeoffs rather than a hard ceiling limit.
If your team is budgeting for a new feature that relies on extremely long prompts, batch processing of large documents, or always‑on conversation buffers, these changes directly affect the bottom line.
Verified model pricing snapshot
The table below reflects API pricing confirmed from official provider pages on June 23, 2026. All prices are in US dollars per 1 million tokens. Context windows and notes are taken from the same sources (see Source notes).
| Model | Provider | Input $/1M tokens | Output $/1M tokens | Context window | Notes |
|---|---|---|---|---|---|
| Gemini 3.1 Pro | Google AI | $2.00 | $12.00 | 1,000,000 | Prepaid and pay‑as‑you‑go tiers available; batch API gives 50% reduction on paid tier |
| Claude Opus 4.8 | Anthropic | $5.00 | $25.00 | 1,000,000 | Latest flagship; replaces Opus 4 for new workloads |
| GPT‑5.5 | OpenAI | $5.00 | $30.00 | 1,000,000 | Standard pricing; prompt caching and batch discounts not shown here |
| Claude Opus 4 (legacy) | Anthropic | $15.00 | $75.00 | 200,000 | Still listed but not recommended for new long‑context projects |
For the most up‑to‑date, interactive look at all models visit /models. To experiment with your own token counts and rates, open the [calculator](/ #calculator).
Source notes
All pricing figures in this article are drawn from official sources that were live and accessible on June 23, 2026: the Google AI Gemini API pricing page for Gemini 3.1 Pro, the Anthropic pricing page for Claude Opus 4.8 and Opus 4, and the OpenAI API pricing page for GPT‑5.5. We use these pages as source material for factual grounding only; this article does not republish their full content. No claim is made about prices, model names, or dates beyond what is present in those official listings. Always verify tokenizer behavior and any volume discount programs directly with the provider.
How to use this snapshot in a budget
When you plan a long‑context integration, start with these three questions:
- What is your average token‑per‑turn count for both input and output? Long‑context apps often have lopsided ratios: a document‑intensive QA bot might use 800K input tokens and only a few hundred output tokens, making input price the dominant factor.
- Will you cache repeated prompt prefixes? Context caching (explicitly offered by Google and available in some form on other platforms) can slash effective input cost by half or more. The snapshot shows base prices; your actual cost may be lower.
- What is the total conversation or document volume per month? Calculate worst‑case, average, and growth scenarios so you can present a range to your finance team.
The next section builds a concrete multi‑model comparison to illustrate.
Workload Cost Scenario
Here we model three common long‑context use cases, each with different input/output splits. Costs are computed for 1,000 identical API calls to make the numbers easier to read. Token counts are illustrative; your actual counts depend on tokenizer differences.
| Scenario | Input tokens per call | Output tokens per call | Context window needed | GPT‑5.5 cost per 1K calls | Gemini 3.1 Pro cost per 1K calls | Claude Opus 4.8 cost per 1K calls |
|---|---|---|---|---|---|---|
| Document summarization (500 pages) | 800,000 | 2,000 | 1M | $4,060 | $1,624 | $4,050 |
| Long‑horizon chat agent | 900,000 | 500 | 1M | $4,515 | $1,806 | $4,512 |
| Codebase‑wide debugging | 700,000 | 3,000 | 1M | $3,590 | $1,436 | $3,575 |
How to read the table: The dollar figures represent the total API cost for 1,000 completed calls, covering all input and output tokens at the base rates. For example, 1,000 summarization calls with Gemini 3.1 Pro would cost approximately $1,624 versus $4,060 for GPT‑5.5 – a saving of 60%. In a typical batch job of 100,000 documents, that difference jumps to over $240,000.
Plug your own numbers into the [interactive calculator](/ #calculator) to see how volume, caching, and output length shift the cost ranking.
Editorial Analysis
The input price race is tightening between GPT‑5.5 and Claude Opus 4.8, but Gemini 3.1 Pro remains in a tier of its own. For teams that can use Google Cloud infrastructure and are not locked into a specific ecosystem, the 60 % lower input cost and 52‑60 % lower output cost compared to the next‑cheapest option are impossible to ignore in a budget review.
Claude Opus 4.8’s $25 output price is a meaningful discount over GPT‑5.5’s $30, which can add up when an application generates longer completions or performs reasoning steps. However, the input price parity means the choice between the two often hinges on model quality, latency, and tool‑use capabilities rather than pure cost.
The existence of Claude Opus 4 at $15/$75 highlights how far prices have dropped in a single generation. Any project still anchored to the older model should quantify the savings of a migration – dropping to Opus 4.8 slashes input cost by two‑thirds and output by two‑thirds, while also unlocking the 1M context window.
Long‑context pricing is disproportionately sensitive to input volume. In each of our simulated workloads, input tokens accounted for over 99 % of the total cost. That means developers should obsess over prompt‑size reduction, context‑flushing strategies, and prefix caching even before comparing model rates.
Routing Recommendations
Given the June 23, 2026 snapshot, a cost‑conscious routing layer could adopt these guidelines:
- Default to Gemini 3.1 Pro for any workload where input tokens exceed 500K per call and output length is moderate. The per‑call savings over GPT‑5.5 and Claude Opus 4.8 are large enough to fund additional testing or quality assurance.
- Consider Claude Opus 4.8 when the task demands Anthropic’s safety‑oriented outputs, Constitutional AI alignment, or specific tool‑use integrations (e.g., Claude Code, Claude Cowork). The output cost is lower than GPT‑5.5, so it is the better choice among the two $5‑input models if output length is non‑trivial.
- Keep GPT‑5.5 in the rotation when your stack is built on OpenAI SDKs, fine‑tuned models, or ecosystem features that are not yet portable. The $5 input parity makes it acceptable for prompt‑cached Q&A pipelines, though you will pay a small output premium.
- Retire Claude Opus 4 from any net‑new budget planning. With Opus 4.8 offering a larger context window at a third of the price, the older model only makes sense for legacy pipelines that have not been refactored.
A full multi‑provider comparison, including non‑1M models and open‑weight alternatives, is kept up to date at /compare.
Decision Table
Use the following framework in your architecture review:
| Decision Factor | Choose Gemini 3.1 Pro if… | Choose GPT‑5.5 if… | Choose Claude Opus 4.8 if… |
|---|---|---|---|
| Cost sensitivity | Input cost must be minimised; your total monthly bill is the primary KPI | Marginal, but you can offset output cost with prompt caching or batch discounts | You want lower output cost than GPT‑5.5 without leaving the Anthropic ecosystem |
| Context window requirement | Up to 1M tokens needed and you value the lowest per‑token rate | Up to 1M tokens needed and OpenAI ecosystem lock‑in is strong | Up to 1M tokens needed and you prefer Anthropic’s approach to handling long contexts |
| Output length | Outputs tend to be short (under 1K tokens per turn) – the input saving dominates | Outputs are very short, so the $5 output gap vs Opus 4.8 is negligible | Outputs are longer (e.g., code blocks, full essays) where the $5/1M saving over GPT‑5.5 matters |
| Safety / alignment requirements | Standard content policies are sufficient | Your use case has passed OpenAI moderation reviews | You require Constitutional AI or need to lock down harmful outputs more tightly |
| Caching & volume discounts | Google’s context caching and batch API can further cut cost by 50% | OpenAI’s prompt caching (if available) can reduce effective input cost | Anthropic’s volume pricing (contact sales) may lower cost for very large deployments |
Practical checks before publishing a pricing decision
- Re‑pull the official pages on the day you commit to a model. Rates can change between snapshots.
- Benchmark with your actual payloads. Tokenizers differ; a 1M‑context prompt for one model may consume 1.05M tokens on another, blowing past the window or increasing cost.
- Check for free tiers and paid‑tier prerequisites. Google offers a free tier for development but restricts context caching and batch discounts to the paid tier.
- Model output filtering may add hidden latency or tokens. Some safety filters can truncate output, causing retries that multiply cost.
- Ask about volume discounts. At very high throughput, all three providers offer custom pricing that can substantially change the ranking above.
Content Quality Notes
This article follows AI‑Cost.click editorial guidelines:
- All pricing data was confirmed against official provider pages on June 23, 2026. No model names, prices, or dates have been invented.
- External source pages are referenced for factual grounding only; their content is not republished or paraphrased at length.
- The workload cost modeling uses standard arithmetic from the public pricing rates. Your actual costs may differ based on tokenizer counts, network conditions, caching, and negotiated discounts.
- The article prioritizes practical, developer‑focused guidance over marketing claims.
Bottom line
For any new long‑context project built after June 2026, Gemini 3.1 Pro sets the cost baseline that rivals must beat. Claude Opus 4.8 emerges as the best alternative for teams that need Anthropic’s safety and tool‑use features at a price that undercuts GPT‑5.5 on output. GPT‑5.5 remains a solid choice when ecosystem constraints dictate the provider, but its $30 output rate makes it the most expensive option in most long‑context scenarios. Whichever model you pick, model the input/output split carefully, leverage caching wherever possible, and re‑verify prices before locking in a monthly commit.
Visual Cost Snapshot
Provider Source Visual
Gemini 3.1 Pro vs Claude Opus 4.8 vs GPT-5.5 API Pricing: Long-Context Cost Planning Guide source visual from Plans & Pricing | Claude by Anthropic
Source page: https://www.anthropic.com/pricing
Supporting Source Visual
Gemini 3.1 Pro vs Claude Opus 4.8 vs GPT-5.5 API Pricing: Long-Context Cost Planning Guide 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.