Meta: Llama 3.1 8B Instruct vs Anthropic: Claude Haiku 4.5
Head-to-head API pricing and cost comparison between Meta’s Meta: Llama 3.1 8B Instruct and Anthropic’s Anthropic: Claude Haiku 4.5. Prices auto-refresh daily from OpenRouter.
Meta: Llama 3.1 8B Instruct is 98% cheaper for input tokens; Meta: Llama 3.1 8B Instruct also wins on output tokens.
Side-by-side comparison
| Spec | Meta: Llama 3.1 8B Instruct | Anthropic: Claude Haiku 4.5 |
|---|---|---|
| Input price (per 1M) | $0.02 | $1.00 |
| Cached input (per 1M) | — | $0.10 |
| Output price (per 1M) | $0.05 | $5.00 |
| Batch input (per 1M) | — | $0.50 |
| Batch output (per 1M) | — | $2.50 |
| Reasoning price (per 1M) | — | — |
| Context window | 16K | 200K |
| Vision support | No | Yes |
| Caching support | No | Yes |
| Batch API | No | Yes |
| Reasoning capability | No | No |
Monthly cost at volume
Estimated monthly API spend at common production traffic levels (input/output tokens per request shown).
| Volume | Meta: Llama 3.1 8B Instruct | Anthropic: Claude Haiku 4.5 | Savings |
|---|---|---|---|
1K req/day 500in / 200out tokens | $0.60 | $45.00 | $44.40 Meta: Llama 3.1 8B Instruct wins |
10K req/day 1500in / 500out tokens | $16.50 | $1,200 | $1,184 Meta: Llama 3.1 8B Instruct wins |
100K req/day 3000in / 800out tokens | $300.00 | $21,000 | $20,700 Meta: Llama 3.1 8B Instruct wins |
1M req/day 8000in / 2000out tokens | $7,800 | $540,000 | $532,200 Meta: Llama 3.1 8B Instruct wins |
Adjust input/output token counts, request volume, batch & cached pricing.
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Frequently asked questions
Which is cheaper, Meta: Llama 3.1 8B Instruct or Anthropic: Claude Haiku 4.5?
For input tokens, Meta: Llama 3.1 8B Instruct is roughly 98% cheaper at $0.02/1M vs $1.00/1M. For output tokens, Meta: Llama 3.1 8B Instruct wins at $0.05/1M. Real-world cost depends on your input/output ratio — use the calculator to model your actual workload.
What’s the context window difference?
Meta: Llama 3.1 8B Instruct has a context window of 16K tokens. Anthropic: Claude Haiku 4.5 offers 200K tokens. Larger context windows are valuable for long documents, RAG pipelines, and multi-turn conversations — but they come with higher input-token bills if you fill them every request.
Should I use Meta: Llama 3.1 8B Instruct or Anthropic: Claude Haiku 4.5?
Choose Meta: Llama 3.1 8B Instruct if you’re already on the Meta stack, want broad ecosystem support, or prefer its lower input price. Choose Anthropic: Claude Haiku 4.5 for Anthropic’s ecosystem, native vision input, or its differentiated capabilities. Run a small benchmark on your own prompts before committing — price is only one axis.
How are these prices kept current?
Prices are pulled directly from OpenRouter’s public models API once every 24 hours via a Convex cron job, then normalized to per-1M-token figures. Last refresh: Apr 21, 2026.