API cost decision in 10 seconds

Mistral Large 3 2512 vs Qwen3 235B A22B

Pick Mistral Large 3 2512 when budget and context both matter.

Page updated: 2026-05-26T15:08:08Z Data confirmed: 2026-05-26T20:08:05Z Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick Mistral Large 3 2512 when budget and context both matter.

On the standard 1M input plus 500K output workload, Mistral Large 3 2512 is estimated at $1.25 vs $1.36 for Qwen3 235B A22B, saving $0.11 (8.4% lower).

Cost-first pickMistral Large 3 2512

Context-first pickMistral Large 3 2512

Sample savings$0.118.4%

10x traffic gap$1.15

Mistral Large 3 2512 is cheaper on the standard workload and also has the larger context window. At 10x that traffic, the same price gap is about $1.15. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Cost winner changes by workload shape: input-heavy / RAG favors Qwen3 235B A22B, balanced workload favors Mistral Large 3 2512, and output-heavy chatbot favors Mistral Large 3 2512.

Workload shape	Token mix	Better pick	Mistral Large 3 2512	Qwen3 235B A22B
Input-heavy / RAG	5M input + 500K output	Qwen3 235B A22B	$3.25	$3.19
Balanced workload	1M input + 1M output	Mistral Large 3 2512	$2	$2.27
Output-heavy chatbot	1M input + 5M output	Mistral Large 3 2512	$8	$9.55

Cheaper input Qwen3 235B A22B $0.5 vs $0.455 / 1M

Qwen3 235B A22B is $0.04 cheaper per 1M input tokens (9% lower; 1.1x difference).

Cheaper output Mistral Large 3 2512 $1.5 vs $1.82 / 1M

Mistral Large 3 2512 is $0.32 cheaper per 1M output tokens (17.6% lower; 1.21x difference).

Larger context Mistral Large 3 2512 262.14K vs 131.07K

Mistral Large 3 2512 has 131.07K more context (2x larger).

Sample workload Mistral Large 3 2512 $1.25 vs $1.36

Mistral Large 3 2512 is $0.11 cheaper on the standard workload (8.4% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.

Monthly input tokens Monthly output tokens

Mistral Large 3 2512 Calculating… Estimated API cost

Qwen3 235B A22B 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

Qwen3 235B A22B has the lower input price; Mistral Large 3 2512 has the lower output price; Mistral Large 3 2512 offers the larger context window. For the 1M input plus 500K output sample, Mistral Large 3 2512 is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $1.25 for Mistral Large 3 2512 and $1.36 for Qwen3 235B A22B.

Best Fit

Choose Mistral Large 3 2512 when you care most about lower output-token price, and larger context window.

Choose Qwen3 235B A22B when you care most about lower input-token price.

Decision Notes

On the standard 1M input plus 500K output workload, Mistral Large 3 2512 is estimated at $1.25 vs $1.36 for Qwen3 235B A22B, saving $0.11 (8.4% lower).
Mistral Large 3 2512 is $0.11 cheaper on the standard workload (8.4% lower).
Qwen3 235B A22B is $0.04 cheaper per 1M input tokens (9% lower; 1.1x difference).
Mistral Large 3 2512 is $0.32 cheaper per 1M output tokens (17.6% lower; 1.21x difference).
Mistral Large 3 2512 has 131.07K more context (2x larger).

Head-to-Head Specs

Feature	Mistral Large 3 2512 (Mistral)	Qwen3 235B A22B (Qwen)
Input Price prompt tokens per 1M	$0.5	$0.455
Completion Price per 1M tokens	$1.5	$1.82
Sample Workload Cost 1M input + 500K output	$1.25	$1.36
Context Window	262.14K	131.07K
Release Date	2025-12-01	2025-04-28
Popularity	#114	#131

Use-Case Decision Matrix

Use case	Better pick	Why
Budget-constrained production	Mistral Large 3 2512	On the standard 1M input plus 500K output workload, Mistral Large 3 2512 is estimated at $1.25 vs $1.36 for Qwen3 235B A22B, saving $0.11 (8.4% lower).
High-volume input processing	Qwen3 235B A22B	Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbots	Mistral Large 3 2512	Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document work	Mistral Large 3 2512	A 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 Large 3 2512 when lower sample workload cost matters most: $0.04.
Mistral Small 3 can replace Mistral Large 3 2512 when lower sample workload cost matters most: $0.09.
Ministral 3 3B 2512 can replace Mistral Large 3 2512 when lower sample workload cost matters most: $0.15.
Mistral Small 3.2 24B can replace Mistral Large 3 2512 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.
Owl Alpha offers 1.05M context with $0 sample workload cost.
DeepSeek V4 Flash offers 1.05M context with $0.2 sample workload cost.
DeepSeek V4 Pro offers 1.05M context with $0.87 sample workload cost.

Popular competitors

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Hy3 preview · Tencent · #2
Claude Opus 4.7 · Anthropic · #3
Claude Sonnet 4.6 · Anthropic · #4

Cheaper alternatives

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Larger context alternatives

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

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Provider catalogs

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

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Mistral catalog

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

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Qwen catalog

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

Open Qwen models

Mistral Large 3 2512

Mistral Large 3 2512 is Mistral’s most capable model to date, featuring a sparse mixture-of-experts architecture with 41B active parameters (675B total), and released under the Apache 2.0 license.

Qwen3 235B A22B

Qwen3-235B-A22B is a 235B parameter mixture-of-experts (MoE) model developed by Qwen, activating 22B parameters per forward pass. It supports seamless switching between a "thinking" mode for complex reasoning, math, and...