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Long Context Cost Calculator
October 2026 8 min read
Free long context cost calculator with 2026 per-token pricing for OpenAI, Anthropic, Google Gemini, DeepSeek, Mistral and xAI. See blended cost per 1K and 1M tokens, tokens per dollar, affordable volume and budget headroom with cache and batch savings. No sign-up.
- Blended / 1K
- $0.000165
- $0.1645 / 1M
- Tokens per $1
- 6,078,431
- GPT-4o mini
- Monthly
- $29.96
- 5,000 req/mo
- Budget
- A+ · Comfortable
- within budget
Unit economics at a glance
Affordable requests
333,750
at $2,000 budget
Context used
6.2%
938,000 tok headroom
Cache saves
$15.75
per month
Batch saves
$5.29
per month
Your numbers — results update live
Input tok / req
Output tok / req
Requests / mo
Monthly budget ($)
Context limit (tok)
Model
Cache hit rate: 50%
Batch share: 30%
Live estimate
- Monthly
- $29.96
- Annual
- $364.54
- Per request
- $0.005992
- Affordable reqs
- 333,750
- Over / under
- $1,970.04 under
Input share97% in / 3% out
Model & provider comparison for this workload
| Provider · Model | Input $/1M | Output $/1M | Monthly | Note |
|---|---|---|---|---|
| OpenAI · text-embedding-3-smallCHEAPEST | $0.020 | $0.000 | $3.31 | Embeddings — input only. |
| Google · Gemini 1.5 Flash | $0.075 | $0.300 | $14.98 | Cheapest big-lab model. |
| OpenAI · GPT-4o mini | $0.150 | $0.600 | $29.96 | Budget default for most tasks. |
| Anthropic · Claude 3 Haiku | $0.250 | $1.250 | $52.06 | Fast + cheap Anthropic tier. |
| Meta (self-host) · Llama 3.1 70B* | $0.350 | $0.400 | $61.41 | *Hosting cost via Together/Fireworks ≈. |
| DeepSeek · DeepSeek V3 | $0.500 | $2.000 | $99.88 | Open-weight value leader. |
| DeepSeek · DeepSeek R1 | $0.550 | $2.190 | $109.78 | Reasoning model, MoE efficient. |
| Google · Gemini 1.5 Pro | $1.250 | $5.000 | $249.69 | Huge context, cache discounts. |
| Mistral · Mistral Large | $2.000 | $6.000 | $382.50 | EU-hosted flagship. |
| xAI · Grok 2 | $2.000 | $10.000 | $416.50 | X-integrated flagship. |
| OpenAI · GPT-4o | $2.500 | $10.000 | $499.38 | Flagship multimodal default. |
| Anthropic · Claude 3.5 Sonnet | $3.000 | $15.000 | $624.75 | Best reasoning / coding mix. |
Same workload priced on every model — only per-token rates change. Output billed higher everywhere. Prices: Oct 2026 public API tiers; batch (−50%) and cache (−50–75%) not applied.
Cost-optimization checklist
Tick off what already ships — this page scores 1/6 (17%).
Learn — AI costs, deeply
An AI bill is requests × tokens × rate × rounds. Providers publish per-token prices, but your architecture decides the multipliers: which tier answers, how much context you send, whether prefixes hit cache, whether async work uses batch, and how many tool-call rounds each task needs. Master those five levers and most teams cut 50–90% without changing what users see.
1 · How per-token pricing really works (and the formula)
cost/request = (input tok ÷ 1M × $in) + (output tok ÷ 1M × $out)
monthly = cost/request × requests/day × 30 × rounds-multiplier
blended $/1M = in% × $in + out% × $out · batch ≈ ×0.5 · cached input ≈ ×0.25–0.5
monthly = cost/request × requests/day × 30 × rounds-multiplier
blended $/1M = in% × $in + out% × $out · batch ≈ ×0.5 · cached input ≈ ×0.25–0.5
Tier first
25–100× range
GPT-4o ($10/1M out) vs mini ($0.60/1M out) on identical tokens. Routing beats haggling.
Outputs next
2–5× input
Capped completions, structured outputs, and fewer agent rounds cut the dearest tokens.
Volume last
linear × discounts
Cache, batch, and annual commits bend the curve once architecture is lean.
Honest limits: public rates move quarterly and hide add-ons (throughput units, private endpoints, support). Treat calculator outputs as planning estimates — reconcile monthly against provider invoices and own logs.
2 · Token math — count before you pay
| Unit | ≈ Tokens | Rule of thumb |
|---|---|---|
| 1,000 words (English) | ~1,330 | words × 1.33 |
| 4,000 characters | ~1,000 | chars ÷ 4 |
| 1024×1024 image | ~250–1,000 | rises with detail / tiling |
| 1 min audio (STT) | ~3–6K* | *often billed per-minute instead |
| Chat turn (support) | ~1.5–3K | history + system + answer |
Measure your own p50/p95 per endpoint for a week — averages hide the long-tail threads that dominate spend. Code and non-English text tokenize denser; always sample real traffic, not docs examples.
3 · Factor-impact table — what moves the bill most
| Factor | Typical impact | Why |
|---|---|---|
| Model tier choice | Very high | Flagship vs mini/flash is a 25–100× swing on identical tokens. Route by difficulty. |
| Output token volume | Very high | Output costs 2–5× input and compounds with agents (3–5 rounds per task). Cap max_tokens. |
| Prompt bloat | High | Stuffed context is billed every call. Retrieve (RAG) instead of pasting whole docs. |
| Cache hit rate | High | Cached system prompts/docs bill at 25–50%. Stable prefixes = free money. |
| Batch vs real-time | High | Batch APIs cost ~50% for async work (ETL, evals, moderation, backfills). |
| Tool-call loops | Medium–high | Each agent round re-sends history. Compact transcripts + stop conditions. |
| Multimodal inputs | Medium–high | One image ≈ 250–1,000 tokens; a minute of audio ≈ 3–6K tokens. |
| Retries / errors | Medium | Partial generations still bill. Timeouts + idempotency keys stop double-spend. |
| Over-provisioned context | Medium | 1M-context calls tempt 10× prompts. Only send what the task needs. |
| Region / egress | Low | Minor vs token cost, but private endpoints + data-residency add fixed fees. |
| Fine-tune hosting | Low–medium | Inference billed like base model; savings come from shorter prompts, not cheaper tokens. |
| Self-host fixed cost | Lever | GPU $/hr beats API $/token only past break-even volume — model it before migrating. |
Cache + batch playbook (fastest payback)
- • Freeze stable prefixes (system prompt, tools schema, top docs) so cache hits.
- • Route evals, moderation, backfills and nightly ETL to Batch API (−50%).
- • Log cache-hit % weekly; a 60%+ hit rate halves effective input price.
- • Keep temperature/format stable — churned phrasing busts cache silently.
GPU-vs-API break-even playbook
- • GPU monthly ≈ $/hr × 730 × utilization. API monthly ≈ tokens × blended rate.
- • Example: RTX 4090 at $0.49/hr ≈ $358/mo — beats GPT-4o mini only past ~600M tok/mo.
- • Add engineering cost: drivers, scaling, evals, on-call. APIs bundle that.
- • Re-run quarterly — rate cuts keep moving the break-even point upward.
4 · TCO: the bill beyond tokens (4-week measurement protocol)
- Week 1 — instrument: log input/output tokens, model, cache-hit, latency per request.
- Week 2 — change one lever (tier router, cache, batch, or caps) on a canary slice.
- Score = (new − old) ÷ old on cost/request, quality (evals), and p95 latency.
- Add people + infra: TCO = API + GPU/hosting + vector DB + evals + support deflection value.
- Hold a control slice — provider-side changes move everyone at once; diff against it.
5 · RAG vs fine-tuning, honestly priced
- RAG pay-as-you-go — embeddings (~$0.02/1M) + vector DB ($20–200/mo) + retrieved tokens per query. Wins when knowledge changes or needs citations.
- Fine-tuning upfront — training tokens billed once (e.g. mini ≈ $3/1M) + eval + shorter prompts forever. Wins for stable style/format/reasoning.
- Combine them — fine-tune the format, retrieve the facts. Most production systems do both.
- YMYL note — calculators here are planning estimates, not procurement or legal advice; validate against contracts.
6 · Benchmarks & standards we cite
| Workload | Typical $/mo | Tokens |
|---|---|---|
| Side-project chatbotGPT-4o mini / Haiku tier. | $5–$25 | ~5M / mo |
| Growing SaaS copilotMixed mini + flagship routing. | $150–$800 | ~100M / mo |
| Support automation (10K chats)Model choice = 10× swing. | $120–$1,900 | ~250M / mo |
| Doc-processing pipelineBatch API halves this. | $60–$400 | ~200M / mo |
| Enterprise RAG rolloutCache + self-host evaluated. | $2K–$25K | ~2B / mo |
- Provider pricing pages (OpenAI, Anthropic, Google AI, DeepSeek, Mistral, xAI) — per-token source of truth
- NIST AI Risk Management Framework (AI 100-1) — cost-aware deployment & monitoring
- ISO/IEC 42001:2023 — AI management system (budget, roles, measurement)
- Schema.org WebApplication + FAQPage + HowTo — structured data used on these pages
- WCAG 2.2 AA — accessible calculator UX (labels, contrast, keyboard)
Frequently Asked Questions
10 answers covering pricing, tokens, models, caching, batch, RAG, GPUs and budgeting — each targets an exact search phrase.
How much does long context cost per month?
It depends on tokens per request, requests per month and model tier. Enter your volume above and the page prices it on all 12 models at 2026 rates, with cache and batch savings applied. Most prototype workloads land under 25 dollars per month on mini tier models.
How is long context calculated?
Blended cost per 1K tokens equals input share times input rate plus output share times output rate. Monthly cost equals that rate times tokens per request times requests per month, minus cache discounts on the cached input share and 50 percent off the batch share. Tokens per dollar is simply 1000 divided by cost per 1K.
How many tokens do I get per dollar?
Divide 1000 by your blended cost per 1K tokens. On GPT-4o mini at an 80/20 mix that is roughly 4M tokens per dollar, while on a flagship it is near 250K. This page shows tokens per dollar for your exact mix and model live.
What do cost per 1K and per 1M tokens mean?
They are the same rate at different scales: per 1M equals per 1K times 1000. Providers quote per 1M because production volumes run in millions. Both figures update live above from your input and output rates.
How much do cached tokens save?
Cached input bills near 30 percent of standard input price. With a 40 percent hit rate expect about 28 percent off the input portion, often 20 to 35 percent off the total bill. Raise the cache slider to model your prefix stability.
Is batch really 50 percent cheaper?
Yes for async work. OpenAI, Anthropic and Google price batch near half of real time with minutes to hours turnaround. Move evals, moderation, backfills and nightly ETL to batch and keep user facing chat real time. The batch slider blends both shares.
How do I stay inside a token budget?
Set budget caps per key and per user with 50/80/100 percent alerts, route simple queries to mini tier, cache stable prefixes and move async jobs to batch. This page grades your volume against your budget and sizes affordable requests.
What is a healthy budget guardrail?
Budget expected monthly times 1.3 headroom with alerts at 50, 80 and 100 percent. Treat over 100 percent as switch default tier now, not buy more credits. Log tokens per request so spikes trace to an endpoint.
How do context limits affect cost?
You pay for tokens actually sent, not window size, but big windows invite oversized prompts. A 100K stuffed prompt on a flagship costs about 25 cents in input every call. Retrieve top passages instead and keep chat prompts under 4K tokens.
Is this calculator free?
Free forever with no sign up. It runs 100 percent in your browser. Copy results, export CSV or print for budget docs.
Long-tail questions this page answers
Each phrase below maps to a section or FAQ further down — written to match the exact phrasing searchers type.
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