Llama 3.1 70B: token counter & pricing

Meta · approximate, within ±3% of reference · pricing as of 2026-04-26.

Provider: Meta
API model ID: meta-llama/llama-3.1-70b-instruct
Context window: 128,000 tokens
Input price: $0.59 per 1M tokens
Output price: $0.79 per 1M tokens
Tokenizer accuracy: approximate, within ±3% of reference
Pricing as of: 2026-04-26

Open the counter to count tokens for Llama 3.1 70B in real time.

What is Llama 3.1 70B?

Llama 3.1 70B is Meta's mid-tier open-weights model — the size most production Llama deployments use. Strong general capability, 128k context, runs comfortably on a single 8×H100 node or via hosted inference.

How tokens are counted here

Llama 3.1 70B uses the same SentencePiece BPE tokenizer as 405B and 8B. We approximate in your browser, accurate to roughly ±3% of the reference tokenizer for typical English text. Marked ≈±3% in the results table.

Why 70B is the Llama default

For most production workloads:

6× cheaper than 405B with most of the quality.
3× more capable than 8B on hard prompts.
Wide hosting availability — Together, Groq, Fireworks, Replicate, Deepinfra, Cloudflare Workers AI, plus self-hosting on a reasonable GPU box.
Permissive license for commercial use under Meta's Llama 3.1 Community License.

It's the open-source answer to "I want Claude Sonnet quality without the per-token bill" — close enough for many workloads, with the openness benefit.

When to use 70B vs alternatives

vs Llama 405B — choose 70B unless you've measured 405B winning on your specific evals.
vs Llama 8B — choose 70B for general reasoning; 8B for high-volume routing/classification.
vs Mistral Large — Mistral Large is broadly comparable but priced higher; 70B usually wins on cost-per-quality.
vs Claude Sonnet / GPT-4o — Sonnet/GPT-4o still win on instruction-following nuance and tool-use reliability for most tasks. 70B wins on cost and on workloads that need open weights.

Pricing notes

The $0.59 input / $0.79 output per million shown is indicative via Together.ai. Groq offers faster inference at slightly different rates; Fireworks and Deepinfra are similar. Always verify your actual provider.

Common questions

How accurate is the ±3% tokenizer approximation?

We validated against the reference Llama tokenizer on a 10k-prompt corpus: median error under 1%, 95th percentile under 3%. We label "≈±3%" to be conservative. Code-heavy or non-English prompts can drift further.

Does 70B support function calling?

Yes — Llama 3.1 added native tool-use support. Implementation quality varies by hosting provider; verify against your specific deployment.

What context length does 70B actually handle well?

The 128k window is real, but quality degrades on retrieval-heavy tasks past ~32k in independent evaluations. For long-context workloads where you need consistent recall across the full window, Gemini 2.5 Pro is currently the strongest option.

Compare Llama 3.1 70B to other models

Llama 3.1 405B (Meta, $3.50/$3.50)
Llama 3.1 8B (Meta, $0.18/$0.18)
Claude Haiku 4.5 (Anthropic, $0.80/$4.00)
Qwen 2.5 Coder 32B (Alibaba, $0.80/$0.80)
Qwen 2.5 72B (Alibaba, $0.90/$0.90)