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Llama 3.3 70B: token counter & pricing

Meta · exact (uses official tokenizer) · pricing as of 2026-05-31.

Updated · By · Methodology

Provider
Meta
API model ID
meta-llama/Llama-3.3-70B-Instruct-Turbo
Context window
128,000 tokens
Input price
$0.88 per 1M tokens
Output price
$0.88 per 1M tokens
Tokenizer accuracy
exact (uses official tokenizer)
Pricing as of
2026-05-31

Open the counter to count tokens for Llama 3.3 70B in real time.

What is Llama 3.3 70B?

Llama 3.3 70B is Meta's current flagship 70B-class open-weights model as advertised on Together.ai's pricing page. Same Llama tokenizer family as 3.1, improved instruction-following and reasoning behavior, single-rate pricing at $0.88 per 1M tokens (input and output identical, a common pattern for open-weights inference providers).

How tokens are counted here

Llama 3.3 70B is one of the few open-weights models here with exact counts. We ship the real Llama BPE tokenizer to your browser via llama-tokenizer-js and run it locally, so the merges and vocabulary match the reference implementation rather than approximating it. The trade-off is payload: the tokenizer data is a roughly 2MB chunk, lazy-loaded only when you select a Llama model so the rest of the site stays light. A side benefit: your text never leaves the browser for this model, no API round-trip involved.

Pricing notes

$0.88 input / $0.88 output per 1M (Together.ai indicative pricing, verify your actual provider).

Single-rate pricing means output-heavy workloads (long generation) cost the same per-token as input-heavy workloads (long RAG context). That's structurally different from OpenAI / Anthropic, where output typically costs 4-10× input.

For 1,000 input + 200 output: $0.00106 per call, $1,056 per 1M calls.

When to use Llama 3.3 70B

When not to use it:

What Llama 3.3 70B costs in production

The decision this model forces is hosted versus self-hosted. Say an internal engineering assistant pushes 600M input and 150M output tokens a month. On Together's listed rate that is 750M total × $0.88 = $660 a month (Together's pricing; other hosts differ, sometimes substantially). The self-host comparison: GPU hardware capable of serving a 70B model at production latency typically rents for several times that figure per month before you count on-call coverage and serving-stack engineering. At this volume the hosted API wins; self-hosting starts paying off when token volume grows several-fold, or when data cannot leave your network at any price.

For reference points on either side: GPT-4o mini would run about $180 ($90 + $90) on the same traffic, and Claude Sonnet 4.6 about $4,050 ($1,800 + $2,250).

Migrating from Llama 3.1 70B

This is the easy kind of migration: same tokenizer family, same 128k context, same prompt format. On Together, swap to apiId meta-llama/Llama-3.3-70B-Instruct-Turbo. Pricing changes shape more than size: 3.1 70B listed at $0.59 input / $0.79 output, while 3.3 is a flat $0.88/$0.88, so input-heavy workloads get slightly more expensive and output-heavy ones roughly break even. Because both models share the Llama BPE vocabulary, the exact in-browser counts on this page apply to either model unchanged, so no re-budgeting of token estimates is needed.

Llama 3.3 70B vs the obvious alternative

GPT-4o mini is the proprietary model most teams weigh against a hosted 70B: $0.15/$0.60 against Llama's flat $0.88, so the OpenAI model is cheaper on raw list price, while Llama offers open weights, fine-tuning rights, and the freedom to switch inference providers. If you are sizing up the bigger end of the Llama line against OpenAI instead, see Llama 3.1 405B vs GPT-4o.

Common questions

Llama 3.3 70B vs Llama 3.1 70B?

3.3 is Together's current advertised flagship Meta model. 3.1 70B is no longer prominently listed on Together's pricing page (kept in this catalog for SEO and for users who still call it on other providers). For new work, default to 3.3.

Llama 3.3 vs Qwen 3 Coder 480B for code?

Qwen 3 Coder 480B ($2/$2 Together) is specifically tuned for code generation, outperforms Llama 3.3 on coding benchmarks but is more expensive. For general-purpose work, Llama 3.3 is the better default.

Where's Llama 4?

Not currently on Together's published pricing page as of April 2026. We don't ship Llama 4 entries to avoid pretending it's available where it isn't. If your provider offers Llama 4, the tokenizer is similar enough to 3.x that this counter's estimate will be close, but verify.

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