Is Claude Haiku 4.5 cheaper than Llama 3.1 70B?

On input tokens, Llama 3.1 70B is cheaper ($0.88 vs $1.00 per million). On output tokens, Llama 3.1 70B is cheaper. Total cost on a typical workload depends on your input:output ratio.

Which has the larger context window, Claude Haiku 4.5 or Llama 3.1 70B?

Claude Haiku 4.5 has a 200,000-token context window. Llama 3.1 70B fits 128,000 tokens.

How do I switch from Claude Haiku 4.5 to Llama 3.1 70B?

Update the model id in your API call: replace "claude-haiku-4.5" with "llama-3.1-70b". Then verify cost impact on a sample of your real prompts using a token counter.

Claude Haiku 4.5 vs Llama 3.1 70B

Side-by-side comparison of Claude Haiku 4.5 (Anthropic) and Llama 3.1 70B (Meta). Exact API pricing per million tokens, context windows, output speed, and total cost on real-world prompts.

Specifications

Spec	Claude Haiku 4.5	Llama 3.1 70B
Provider	Anthropic	Meta
Model id	claude-haiku-4.5	llama-3.1-70b
Input price (per 1M tokens)	$1.00	$0.88
Output price (per 1M tokens)	$5.00	$0.88
Context window	200,000	128,000
Output speed (tokens/sec)	~150	~75

Cost on real prompts

Total cost = (input tokens × input price) + (output tokens × output price). Numbers below use the exact pricing tables published by each provider.

Scenario	Input	Output	Claude Haiku 4.5	Llama 3.1 70B	Cheaper
Short question + answer	50	150	$0.0008	$0.000176	Llama 3.1 70B
Code review on one file	500	1,500	$0.008000	$0.001760	Llama 3.1 70B
Long document summary	5,000	500	$0.007500	$0.004840	Llama 3.1 70B
Heavy reasoning task	2,000	8,000	$0.042000	$0.008800	Llama 3.1 70B
Full codebase analysis	50,000	10,000	$0.100000	$0.052800	Llama 3.1 70B

Want the exact cost for your prompt instead of these examples? Open the cost calculator pre-loaded with both models →

When to pick which

Heuristics derived from the spec table above. Always validate on your own prompts before committing — these are starting points, not verdicts.

Pick Claude Haiku 4.5 for

•tasks needing a larger context window — claude-haiku-4.5 fits 2x more tokens than llama-3.1-70b
•latency-sensitive UX (chat, autocompletion) — claude-haiku-4.5 streams faster (~150 vs ~75 tok/s)

Pick Llama 3.1 70B for

•output-heavy workloads — llama-3.1-70b is meaningfully cheaper per output token

Switching between them

For most use cases, switching providers means updating the model id and the request shape if the providers differ. Within the same provider, it's usually a single-line change.

From Claude Haiku 4.5 to Llama 3.1 70B

# Before
model = "claude-haiku-4.5"

# After
model = "llama-3.1-70b"

If the providers differ (Anthropic vs Meta), you'll also need to swap the SDK / endpoint URL. Cross-provider migrations usually take 30 minutes to a few hours depending on how many features (streaming, function calling, tool use) you depend on.

Calculate cost on your own prompt

The examples above use generic input/output ratios. For an exact comparison, paste your real prompt into the calculator — it counts tokens with the right tokenizer for each model and shows side-by-side cost.

Open the calculator with both models →