Monthly cost at list price for five common workloads. Token counts per unit are stated so you can scale them to your own traffic. A workload that does not fit a model's context window is marked rather than priced.
Workload (per month)
Mistral: Mistral Medium 3.5
Pareto 26.9
Difference
Customer-support chatbot
50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens)
$263
$338
Mistral: Mistral Medium 3.5 saves $75.00
RAG search over documents
20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens)
$255
$375
Mistral: Mistral Medium 3.5 saves $120
Coding / tool-using agent
2,000 tasks — each a multi-step task with tool calls and file context (60,000 in / 6,000 out tokens)
$270
$390
Mistral: Mistral Medium 3.5 saves $120
Batch summarisation
10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens)
$165
$245
Mistral: Mistral Medium 3.5 saves $80.00
Long-document analysis
500 documents — each a 150-page contract or codebase read in one pass (100,000 in / 2,000 out tokens)
$82.50
$133
Mistral: Mistral Medium 3.5 saves $50.00
Capabilities side by side
Capability
Mistral: Mistral Medium 3.5
Pareto 26.9
What it lets you do
Tool / function calling
✓ Yes
✓ Yes
call your APIs and run agent loops
Structured output (JSON schema)
✓ Yes
✓ Yes
return JSON that validates against your schema
Reasoning / thinking mode
✓ Yes
—
spend extra tokens thinking before answering hard problems
Image input
✓ Yes
✓ Yes
read screenshots, charts and scanned pages
Audio input
—
—
take speech or audio directly
File / PDF input
✓ Yes
—
accept documents without your own parsing step
Image output
—
—
generate images, not just text
Built-in web search
—
—
answer from live web results
Seed (reproducible sampling)
✓ Yes
—
repeat a generation for tests and evals
Log-probabilities
—
—
read token confidence for classification and scoring
From each model's published parameters and input/output types. “—” means not advertised, not proven absent.
Price history
Mistral: Mistral Medium 3.5: price unchanged since we started tracking it on 2026-08-06.
Pareto 26.9: price unchanged since we started tracking it on 2026-10-14.
Which should you choose?
Choose Mistral: Mistral Medium 3.5 if…
cost matters at scale — it is 20% cheaper on a typical 3:1 input/output mix ($263 vs $338 a month for 50,000 support chats)
you need reasoning / thinking mode — to spend extra tokens thinking before answering hard problems; Pareto 26.9 does not advertise it
you need file / PDF input — to accept documents without your own parsing step; Pareto 26.9 does not advertise it
you need seed (reproducible sampling) — to repeat a generation for tests and evals; Pareto 26.9 does not advertise it
you generate long outputs in one go — up to 210K tokens (about 157,300 words) per response
Choose Pareto 26.9 if…
you want the newer model — released 2026-10-13, 6 months after the other
Frequently asked questions
Is Mistral: Mistral Medium 3.5 better than Pareto 26.9?
Neither wins outright: Mistral: Mistral Medium 3.5 is the pick when cost matters at scale, Pareto 26.9 when you want the newer model. In short: Mistral: Mistral Medium 3.5 when cost matters at scale, you need reasoning / thinking mode, you need file / PDF input, you need seed (reproducible sampling), you generate long outputs in one go; Pareto 26.9 when you want the newer model.
Which is cheaper, Mistral: Mistral Medium 3.5 or Pareto 26.9?
Mistral: Mistral Medium 3.5 is cheaper: $3.00 vs $3.75 per 1M tokens at a typical 3:1 input/output mix, 20% less. For 50,000 support conversations a month that is $263 against $338.
How much do Mistral: Mistral Medium 3.5 and Pareto 26.9 cost per million tokens?
Mistral: Mistral Medium 3.5 costs $1.50 per 1M input tokens and $7.50 per 1M output tokens; Pareto 26.9 costs $2.50 input and $7.50 output. Output tokens are billed separately, so long answers shift the gap.
Which has the bigger context window, Mistral: Mistral Medium 3.5 or Pareto 26.9?
Both take 262K tokens per request — roughly 393 pages of text.
Do Mistral: Mistral Medium 3.5 and Pareto 26.9 support function calling and JSON output?
Mistral Medium 3.5 is a dense 128B instruction-following model from Mistral AI. It supports text and image inputs with text output, and is designed for agentic workflows, coding, and complex...
Pareto is a multimodal composite model built for research, coding, and agentic workflows, while delivering frontier-level performance across a broad range of general-purpose tasks.