Is GPT-5 cheaper than Mistral Large?

On input tokens, GPT-5 is cheaper ($1.25 vs $4.00 per million). On output tokens, GPT-5 is cheaper. Total cost on a typical workload depends on your input:output ratio.

Which has the larger context window, GPT-5 or Mistral Large?

GPT-5 has a 400,000-token context window. Mistral Large fits 128,000 tokens.

How do I switch from GPT-5 to Mistral Large?

Update the model id in your API call: replace "gpt-5" with "mistral-large". Then verify cost impact on a sample of your real prompts using a token counter.

GPT-5 vs Mistral Large

Side-by-side comparison of GPT-5 (OpenAI) and Mistral Large (Mistral). Exact API pricing per million tokens, context windows, output speed, and total cost on real-world prompts.

Specifications

Spec	GPT-5	Mistral Large
Provider	OpenAI	Mistral
Model id	gpt-5	mistral-large
Input price (per 1M tokens)	$1.25	$4.00
Output price (per 1M tokens)	$10.00	$12.00
Context window	400,000	128,000
Output speed (tokens/sec)	~110	~55

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-5	Mistral Large	Cheaper
Short question + answer	50	150	$0.001563	$0.002000	GPT-5
Code review on one file	500	1,500	$0.015625	$0.020000	GPT-5
Long document summary	5,000	500	$0.011250	$0.026000	GPT-5
Heavy reasoning task	2,000	8,000	$0.082500	$0.104000	GPT-5
Full codebase analysis	50,000	10,000	$0.162500	$0.320000	GPT-5

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-5 for

•input-heavy workloads (long context, RAG, document QA) — gpt-5 is cheaper per input token
•tasks needing a larger context window — gpt-5 fits 3x more tokens than mistral-large
•latency-sensitive UX (chat, autocompletion) — gpt-5 streams faster (~110 vs ~55 tok/s)

Pick Mistral Large 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-5 to Mistral Large

# Before
model = "gpt-5"

# After
model = "mistral-large"

If the providers differ (OpenAI vs Mistral), 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 →