API cost decision in 10 seconds

NewMistral Medium 3.5 vs GPT-5.2-Codex

Pick Mistral Medium 3.5 for lower cost; pick GPT-5.2-Codex only if the larger context window matters more.

Page updated:  Data confirmed:  Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick Mistral Medium 3.5 for lower cost; pick GPT-5.2-Codex only if the larger context window matters more.

On the standard 1M input plus 500K output workload, Mistral Medium 3.5 is estimated at $5.25 vs $8.75 for GPT-5.2-Codex, saving $3.5 (40% lower).

Cost-first pickMistral Medium 3.5
Context-first pickGPT-5.2-Codex
Sample savings$3.540%
10x traffic gap$35

GPT-5.2-Codex has more context, but Mistral Medium 3.5 saves $3.5 on the standard workload. At 10x that traffic, the same price gap is about $35. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Mistral Medium 3.5 stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shapeToken mixBetter pickMistral Medium 3.5GPT-5.2-Codex
Input-heavy / RAG5M input + 500K outputMistral Medium 3.5$11.25$15.75
Balanced workload1M input + 1M outputMistral Medium 3.5$9$15.75
Output-heavy chatbot1M input + 5M outputMistral Medium 3.5$39$71.75
Cheaper input Mistral Medium 3.5 $1.5 vs $1.75 / 1M

Mistral Medium 3.5 is $0.25 cheaper per 1M input tokens (14.3% lower; 1.17x difference).

Cheaper output Mistral Medium 3.5 $7.5 vs $14 / 1M

Mistral Medium 3.5 is $6.5 cheaper per 1M output tokens (46.4% lower; 1.87x difference).

Larger context GPT-5.2-Codex 262.14K vs 400K

GPT-5.2-Codex has 137.86K more context (1.53x larger).

Sample workload Mistral Medium 3.5 $5.25 vs $8.75

Mistral Medium 3.5 is $3.5 cheaper on the standard workload (40% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
Mistral Medium 3.5 Calculating… Estimated API cost
GPT-5.2-Codex Calculating… Estimated API cost
Cheaper for this workload Calculating… Difference: calculating…

This estimate uses normalized public API pricing per 1M tokens. It is a planning aid, not a billing quote. Verify provider pricing, limits, and terms before production use.

Quick Decision

Verdict

Mistral Medium 3.5 has the lower input price; Mistral Medium 3.5 has the lower output price; GPT-5.2-Codex offers the larger context window. For the 1M input plus 500K output sample, Mistral Medium 3.5 is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $5.25 for Mistral Medium 3.5 and $8.75 for GPT-5.2-Codex.

Best Fit

Choose Mistral Medium 3.5 when you care most about lower input-token price, and lower output-token price.

Choose GPT-5.2-Codex when you care most about larger context window.

Decision Notes
  • On the standard 1M input plus 500K output workload, Mistral Medium 3.5 is estimated at $5.25 vs $8.75 for GPT-5.2-Codex, saving $3.5 (40% lower).
  • Mistral Medium 3.5 is $3.5 cheaper on the standard workload (40% lower).
  • Mistral Medium 3.5 is $0.25 cheaper per 1M input tokens (14.3% lower; 1.17x difference).
  • Mistral Medium 3.5 is $6.5 cheaper per 1M output tokens (46.4% lower; 1.87x difference).
  • GPT-5.2-Codex has 137.86K more context (1.53x larger).
Head-to-Head Specs
FeatureNewMistral Medium 3.5
(Mistral)
GPT-5.2-Codex
(OpenAI)
Input Price
prompt tokens per 1M
$1.5$1.75
Completion Price
per 1M tokens
$7.5$14
Sample Workload Cost
1M input + 500K output
$5.25$8.75
Context Window262.14K400K
Release Date

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionMistral Medium 3.5On the standard 1M input plus 500K output workload, Mistral Medium 3.5 is estimated at $5.25 vs $8.75 for GPT-5.2-Codex, saving $3.5 (40% lower).
High-volume input processingMistral Medium 3.5Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsMistral Medium 3.5Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workGPT-5.2-CodexA larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Same-provider lower-cost swaps
  • Mistral Nemo can replace Mistral Medium 3.5 when lower sample workload cost matters most: $0.04.
  • Mistral Small 3 can replace Mistral Medium 3.5 when lower sample workload cost matters most: $0.09.
  • Ministral 3 3B 2512 can replace Mistral Medium 3.5 when lower sample workload cost matters most: $0.15.
  • Mistral Small 3.2 24B can replace Mistral Medium 3.5 when lower sample workload cost matters most: $0.17.
Larger context near this budget
  • Llama 4 Scout offers 10M context with $0.23 sample workload cost.
  • Grok 4.20 Multi-Agent offers 2M context with $5 sample workload cost.
  • Grok 4.20 offers 2M context with $2.5 sample workload cost.
  • GPT-5.4 offers 1.05M context with $10 sample workload cost.

Cheaper alternatives

Review low-cost models sorted by a standard 1M input plus 500K output workload.

Open cheapest models

Larger context alternatives

Find models with larger context windows for RAG, long documents, and codebase review.

Open largest context models

Provider catalogs

Compare models within provider hubs before choosing a final API vendor.

Open provider hubs

Mistral catalog

Review all tracked Mistral models before deciding whether this matchup is the right shortlist.

Open Mistral models

OpenAI catalog

Check other OpenAI models with comparable pricing, context, or release timing.

Open OpenAI models
Mistral Medium 3.5

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...

GPT-5.2-Codex

GPT-5.2-Codex is an upgraded version of GPT-5.1-Codex optimized for software engineering and coding workflows. It is designed for both interactive development sessions and long, independent execution of complex engineering tasks....