Is GPT-4o mini cheaper than Llama 3.1 70B?

On input tokens, GPT-4o mini is cheaper ($0.15 vs $0.88 per million). On output tokens, GPT-4o mini is cheaper. Total cost on a typical workload depends on your input:output ratio.

Which has the larger context window, GPT-4o mini or Llama 3.1 70B?

Llama 3.1 70B has a 128,000-token context window. GPT-4o mini fits 128,000 tokens.

How do I switch from GPT-4o mini to Llama 3.1 70B?

Update the model id in your API call: replace "gpt-4o-mini" with "llama-3.1-70b". Then verify cost impact on a sample of your real prompts using a token counter.

GPT-4o mini vs Llama 3.1 70B

Side-by-side comparison of GPT-4o mini (OpenAI) 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	GPT-4o mini	Llama 3.1 70B
Provider	OpenAI	Meta
Model id	gpt-4o-mini	llama-3.1-70b
Input price (per 1M tokens)	$0.15	$0.88
Output price (per 1M tokens)	$0.60	$0.88
Context window	128,000	128,000
Output speed (tokens/sec)	~130	~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	GPT-4o mini	Llama 3.1 70B	Cheaper
Short question + answer	50	150	$0.000097	$0.000176	GPT-4o mini
Code review on one file	500	1,500	$0.000975	$0.001760	GPT-4o mini
Long document summary	5,000	500	$0.001050	$0.004840	GPT-4o mini
Heavy reasoning task	2,000	8,000	$0.005100	$0.008800	GPT-4o mini
Full codebase analysis	50,000	10,000	$0.013500	$0.052800	GPT-4o mini

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 GPT-4o mini for

•output-heavy workloads (long-form generation, code, summaries) — gpt-4o-mini is meaningfully cheaper per output token
•input-heavy workloads (long context, RAG, document QA) — gpt-4o-mini is cheaper per input token
•latency-sensitive UX (chat, autocompletion) — gpt-4o-mini streams faster (~130 vs ~75 tok/s)

Pick Llama 3.1 70B for

No clear advantage on the data points we measure. Compare on your actual prompts.

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 GPT-4o mini to Llama 3.1 70B

# Before
model = "gpt-4o-mini"

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

If the providers differ (OpenAI 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 →