Mistral Medium 3.5 vs o3
Pick up to 3 models · pricing, context and capabilities · simulate your product cost
Data updated on September 30, 2026· 217 models
Mistral Medium 3.5 is a dense 128B instruction-following model from Mistral AI.
- Reasoning estimated
- Speed estimated
- ▲
- Input
- Output
- Context
- ▲262 K tokens
- Max output
- ▲210 K
- Input, $ per 1M tokens
- ▲$1.50
- Output, $ per 1M tokens
- ▲$7.50
- Cache read, $ per 1M
- —
- Batch (input / output)
- $0.750 / $3.75
- Tool calling
- Yes
- Released
- ▲05/01/2026
- Knowledge cutoff
- —
- API id
- mistralai/mistral-medium-3-5
o3 is a well-rounded and powerful model across domains.
- Reasoning estimated
- ▲
- Speed estimated
- Input
- Output
- Context
- 200 K tokens
- Max output
- 100 K
- Input, $ per 1M tokens
- $2.00
- Output, $ per 1M tokens
- $8.00
- Cache read, $ per 1M
- $0.500
- Batch (input / output)
- $1.00 / $4.00
- Tool calling
- Yes
- Released
- 04/16/2025
- Knowledge cutoff
- 2024-05
- API id
- openai/o3
Which is the better API choice: Mistral Medium 3.5 vs o3?
- Mistral Medium 3.5: cheapest input (25% less than o3)
- Mistral Medium 3.5: cheapest output
- Mistral Medium 3.5: largest context (262 K)
- Mistral Medium 3.5: the newest (05/01/2026)
- o3: most reasoning (estimated)
- Mistral Medium 3.5: the fastest (estimated)
Mistral Medium 3.5 costs 25% less on input and 6% less on output than o3 ($1.50 / $7.50 versus $2.00 / $8.00 per million tokens). Mistral Medium 3.5 accepts more context (262 K). On capabilities, o3 has a reasoning mode. Mistral Medium 3.5 and o3 offer a batch tier at half price for jobs that do not need an immediate answer.
What would your product cost?
Estimate the monthly cost with your app's real volume. List prices, no cache or batch discounts.
With Mistral Medium 3.5 you save $9.50 a month versus o3 (15% less).
The 6 cheapest for this scenario (chat, last 12 months)
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- 3Qwen3.7 Flash Qwen$0.97
- 4DeepSeek V4 Flash 0731 DeepSeek$1.55
- 5Ministral 3 3B 2512 Mistral$1.90
- 6Qwen3.5-Flash Qwen$2.02
Paste your prompt: tokens and cost per model
Rough estimate (about 4 characters per token). Nothing is sent to any server.
Cost of sending it 1,000 times (input only)
- 1Mistral Medium 3.5 Mistral$0.114
- 2o3 OpenAI$0.152
- 3Claude Sonnet 5.5 Anthropic$0.152
- 4Claude Sonnet 5 Anthropic$0.152
- 5Claude Opus 5.5 Anthropic$0.304
- 6Claude Opus 5 Anthropic$0.380
- 7Claude Fable 5.1 Anthropic$0.760
- 8Claude Fable 5 Anthropic$0.760
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Frequently asked questions
Which is the better API choice: Mistral Medium 3.5 vs o3?
Mistral Medium 3.5 costs 25% less on input and 6% less on output than o3 ($1.50 / $7.50 versus $2.00 / $8.00 per million tokens). Mistral Medium 3.5 accepts more context (262 K). On capabilities, o3 has a reasoning mode. Mistral Medium 3.5 and o3 offer a batch tier at half price for jobs that do not need an immediate answer.
Which is cheaper: Mistral Medium 3.5 and o3?
Mistral Medium 3.5 is cheaper: $1.50 per million input tokens and $7.50 per million output tokens, versus $2.00 / $8.00 for o3.
Which has more context: Mistral Medium 3.5 and o3?
Mistral Medium 3.5 accepts 262 K tokens of context; o3 accepts 200 K.
Which is newer: Mistral Medium 3.5 and o3?
Mistral Medium 3.5 was released on 05/01/2026; o3 on 04/16/2025.
What would 10,000 requests a month cost with Mistral Medium 3.5 and o3?
With 1,500 input and 400 output tokens per request: Mistral Medium 3.5: $52.50; o3: $62.00 a month, at list price.
Where do the prices come from and how often are they updated?
From the public catalogs of OpenRouter, models.dev and LiteLLM, which publish each provider's list prices. They sync once a day and the last update date is shown above the comparison.
What does price per million tokens mean?
It is what the provider charges per million tokens you send (input) or the model generates (output). A token is roughly 4 characters. Output almost always costs more than input.
Sources: OpenRouter, models.dev and LiteLLM. Prices in USD per million tokens, list price, no volume discounts. Prompt tokens are an estimate; each provider's tokenizer may differ.