Short answer: the documented GPT-4 API pricing ladder was 8K and 32K context—not 4K. Historically, GPT-4 (8,192-token context) cost $30 per 1 million input tokens and $60 per 1 million output tokens. GPT-4-32k (32,768-token context) cost $60 per 1 million input tokens and $120 per 1 million output tokens. Those are token rates, not flat charges for using an entire context window.
This guide separates historical API prices from current availability, explains how to calculate a realistic bill, and outlines migration options. Pricing and model access change; the current-model information below was checked against OpenAI material dated August 16, 2026.
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Historical GPT-4 API prices at a glance
| Model | Context window | Input price | Output price | Per-1K equivalent |
|---|---|---|---|---|
| GPT-4 | 8,192 tokens | $30 per 1M tokens | $60 per 1M tokens | $0.03 input / $0.06 output |
| GPT-4-32k | 32,768 tokens | $60 per 1M tokens | $120 per 1M tokens | $0.06 input / $0.12 output |
These historical rates are documented by OpenAI in its GPT-4 cost guidance and launch material. The 32K model handled four times as much context as the 8K model, while its per-token rates were twice as high.
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OpenAI’s GPT-4 launch description identifies an 8,192-token context for GPT-4 and a 32,768-token context for GPT-4-32k: OpenAI’s GPT-4 research announcement. The cited official pricing material does not list a 4K GPT-4 model.
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The 4K label is commonly a mix-up with another model family. OpenAI described standard GPT-3.5 Turbo as 4K and later introduced a 16K version in its API updates announcement. A third-party provider may also use its own label, but that should not be presented as an official GPT-4 tier.
What “context window” means for your bill
A context window is the maximum combined capacity for material sent to the model and the response it generates. It is not a reservation and not a flat fee. An 8K or 32K request is billed on the tokens actually processed.
- Input tokens: your prompt plus applicable system instructions, conversation history, tool or function schemas, and supplied documents.
- Output tokens: the generated response.
- Combined limit: input and output must fit within the model’s context and endpoint limits. A large prompt leaves less room for the answer.
- Token accounting: billing counts tokens, not characters or words.
For example, a 30,000-token prompt cannot fit in GPT-4’s 8,192-token context. It can fit within GPT-4-32k only if the requested response and all other request material keep the combined total within the applicable limit.
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How to calculate a request
Using the per-million rates:
request cost = (input tokens ÷ 1,000,000 × input rate) + (output tokens ÷ 1,000,000 × output rate)
The same calculation works with per-1K rates by replacing 1,000,000 with 1,000.
Example: 2,000 input tokens and 500 output tokens
| Model | Calculation | Total |
|---|---|---|
| GPT-4 | 2 × $0.03 + 0.5 × $0.06 | $0.09 |
| GPT-4-32k | 2 × $0.06 + 0.5 × $0.12 | $0.18 |
Example: 8,000 input tokens and 1,000 output tokens
| Model | Calculation | Total |
|---|---|---|
| GPT-4 | 8 × $0.03 + 1 × $0.06 | $0.30 |
| GPT-4-32k | 8 × $0.06 + 1 × $0.12 | $0.60 |
Example: 30,000 input tokens and 2,000 output tokens
GPT-4 cannot accept this request within its 8,192-token limit. On GPT-4-32k, the historical calculation is 30 × $0.06 for input plus 2 × $0.12 for output, for a total of $2.04.
Budgeting a monthly API bill
Estimate monthly usage with:
monthly cost = (monthly input tokens ÷ 1,000,000 × input rate) + (monthly output tokens ÷ 1,000,000 × output rate)
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A practical budgeting checklist
- Measure average and peak input tokens separately from output tokens.
- Include retry volume and background jobs, not just successful interactive calls.
- Model a high-usage month rather than multiplying a single “typical” request.
- Set application-side token ceilings and provider spending alerts.
- Track prompt versions so an expanded system instruction does not silently raise costs.
Historical model names and availability
Documents and code may refer to gpt-4, gpt-4-0314, gpt-4-0613, gpt-4-32k, gpt-4-32k-0314, or gpt-4-32k-0613. OpenAI historically used stable aliases that could be upgraded, while dated snapshots were intended for reproducible behavior, as described in its model-update announcement.
As of August 16, 2026, OpenAI’s GPT-4 model page describes GPT-4 as an older model with an 8,192-token context and lists $30 per 1M input tokens and $60 per 1M output tokens. It lists gpt-4-0613 as deprecated and does not present GPT-4-32k as a normal current option. Verify access in your account before planning a new deployment.
API billing is not ChatGPT subscription pricing
The OpenAI API is usage-based: you pay for tokens processed by your account. ChatGPT subscriptions are consumer product plans with their own limits and model availability; they are not a per-token GPT-4 tariff. OpenAI’s release notes state that GPT-4 was retired from ChatGPT on April 30, 2025, while API availability was treated separately: OpenAI’s FAQ.
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Third-party platforms can add markup, minimum charges, bundled limits, or different retention policies. Their advertised “GPT-4” price is therefore not automatically OpenAI’s API price.
When 8K or 32K made economic sense
8K workloads
Historically, 8K was the cheaper choice for ordinary conversations, moderate documents, and prompts that fit after allowing room for instructions and the answer. Its limitation is that chat history, tool schemas, and retrieved text consume the same capacity.
32K workloads
32K was useful for long-document review, larger code excerpts, or histories that could not be kept within 8K without aggressive chunking. The trade-off was twice the historical input and output rate, greater latency and noise risk, and uncertain legacy availability.
Retrieval instead of a larger prompt
Retrieval-augmented generation searches or ranks a document collection and sends only relevant passages. It can lower token use and improve focus, but requires indexing, chunking, retrieval evaluation, and citation handling. Long-context prompting is simpler for some applications but repeatedly sends more data and may include irrelevant material.
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OpenAI’s GPT-4.1 announcement lists the following prices and a 1-million-token context window:
| Model | Input | Cached input | Output | Context |
|---|---|---|---|---|
| GPT-4.1 | $2 per 1M | $0.50 per 1M | $8 per 1M | 1M tokens |
| GPT-4.1 mini | $0.40 per 1M | $0.10 per 1M | $1.60 per 1M | 1M tokens |
| GPT-4.1 nano | $0.10 per 1M | $0.025 per 1M | $0.40 per 1M | 1M tokens |
See the GPT-4.1 announcement for those published signals. It states that long-context requests have no additional long-context surcharge beyond standard rates and that Batch API use receives an additional 50% discount. Recheck the live pricing page before committing because rates and availability can change.
Choose with a workload test
- Measure maximum and typical prompt sizes, output sizes, and monthly volume.
- Evaluate answer quality on representative examples, including long and short inputs.
- Check latency, tool calls, structured-output support, data-handling requirements, and regional availability.
- Compare retrieval, summarization, or context compression with sending the full source each time.
- Confirm lifecycle status and pin a dated model only when reproducibility justifies the maintenance cost.
Cost-control failure modes
- Mislabeling a model: do not invent a GPT-4 4K price from a GPT-3.5 figure.
- Using one headline rate: historical output tokens cost twice the input rate.
- Treating context as a charge: a 32K allowance is not a $120 fee.
- Ignoring history: every resent message can increase input usage.
- Retrying blindly: timeouts and rate-limit retries can create duplicate billable calls.
- Sending full documents repeatedly: consider retrieval, summaries, caching, or Batch processing when appropriate.
- Assuming aliases never change: use dated snapshots only after verifying they remain available and supported.
A sensible migration decision
GPT-4-32k is defensible only if your account still exposes it, a single large context is genuinely required, behavior changes would create unacceptable risk, and testing supports the premium. For a new system, compare a current long-context model such as GPT-4.1, a smaller model, and a retrieval-based design against your own quality and cost measurements. Do not treat historical GPT-4-32k pricing as a recommendation to purchase a legacy model today.
Frequently Asked Questions
Was there an official GPT-4 4K model?
The cited OpenAI GPT-4 materials document 8,192-token GPT-4 and 32,768-token GPT-4-32k variants, not a 4K GPT-4 tier. The 4K label was associated with GPT-3.5 Turbo and may also appear in third-party naming.
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No. $120 was the historical price per 1 million output tokens for GPT-4-32k. Each request is charged for its actual input and output tokens.
Is GPT-4-32k still guaranteed to be available?
No. The current GPT-4 page does not present GPT-4-32k as a normal current option. Check model access and lifecycle status in your account before deployment.
Should I use ChatGPT Plus instead of the API?
They serve different purposes. ChatGPT is a subscription product with usage limits; the API is programmatic, usage-based token billing. Choose based on whether you need an end-user interface or application integration.
The Bottom Line
The accurate comparison is historical GPT-4 8K versus GPT-4-32k—not a three-tier 4K/8K/32K GPT-4 ladder. Budget input and output tokens separately, treat context size as a limit rather than a fee, and evaluate current long-context models or retrieval before building a new system on a legacy GPT-4 variant.
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