API cost decision in 10 seconds

GLM 4.6 vs Mistral Large 3 2512

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

Page updated: 2026-05-26T15:08:08Z Data confirmed: 2026-05-26T18:08:02Z 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 $1.3 for GLM 4.6, saving $0.05 (3.8% lower).

Cost-first pickMistral Large 3 2512

Context-first pickMistral Large 3 2512

Sample savings$0.053.8%

10x traffic gap$0.5

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 $0.5. 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 GLM 4.6, balanced workload favors Mistral Large 3 2512, and output-heavy chatbot favors Mistral Large 3 2512.

Workload shape	Token mix	Better pick	GLM 4.6	Mistral Large 3 2512
Input-heavy / RAG	5M input + 500K output	GLM 4.6	$3.02	$3.25
Balanced workload	1M input + 1M output	Mistral Large 3 2512	$2.17	$2
Output-heavy chatbot	1M input + 5M output	Mistral Large 3 2512	$9.13	$8

Cheaper input GLM 4.6 $0.43 vs $0.5 / 1M

GLM 4.6 is $0.07 cheaper per 1M input tokens (14% lower; 1.16x difference).

Cheaper output Mistral Large 3 2512 $1.74 vs $1.5 / 1M

Mistral Large 3 2512 is $0.24 cheaper per 1M output tokens (13.8% lower; 1.16x difference).

Larger context Mistral Large 3 2512 202.75K vs 262.14K

Mistral Large 3 2512 has 59.39K more context (1.29x larger).

Sample workload Mistral Large 3 2512 $1.3 vs $1.25

Mistral Large 3 2512 is $0.05 cheaper on the standard workload (3.8% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.

Monthly input tokens Monthly output tokens

GLM 4.6 Calculating… Estimated API cost

Mistral Large 3 2512 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

GLM 4.6 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 $1.3 for GLM 4.6 and $1.25 for Mistral Large 3 2512.

Best Fit

Choose GLM 4.6 when you care most about lower input-token price.

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

Decision Notes

On the standard 1M input plus 500K output workload, Mistral Large 3 2512 is estimated at $1.25 vs $1.3 for GLM 4.6, saving $0.05 (3.8% lower).
Mistral Large 3 2512 is $0.05 cheaper on the standard workload (3.8% lower).
GLM 4.6 is $0.07 cheaper per 1M input tokens (14% lower; 1.16x difference).
Mistral Large 3 2512 is $0.24 cheaper per 1M output tokens (13.8% lower; 1.16x difference).
Mistral Large 3 2512 has 59.39K more context (1.29x larger).

Head-to-Head Specs

Feature	GLM 4.6 (Z.ai)	Mistral Large 3 2512 (Mistral)
Input Price prompt tokens per 1M	$0.43	$0.5
Completion Price per 1M tokens	$1.74	$1.5
Sample Workload Cost 1M input + 500K output	$1.3	$1.25
Context Window	202.75K	262.14K
Release Date	2025-09-30	2025-12-01
Popularity	#63	#114

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 $1.3 for GLM 4.6, saving $0.05 (3.8% lower).
High-volume input processing	GLM 4.6	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

GLM 4.5 Air (free) can replace GLM 4.6 when lower sample workload cost matters most: $0.
GLM 4 32B can replace GLM 4.6 when lower sample workload cost matters most: $0.15.
GLM 4.7 Flash can replace GLM 4.6 when lower sample workload cost matters most: $0.26.
GLM 4.5 Air can replace GLM 4.6 when lower sample workload cost matters most: $0.55.

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

DeepSeek V4 Flash · DeepSeek · #1
Hy3 preview · Tencent · #2
Claude Opus 4.7 · Anthropic · #3
Claude Sonnet 4.6 · Anthropic · #4

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

Z.ai catalog

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

Open Z.ai models

Mistral catalog

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

Open Mistral models

GLM 4.6

Compared with GLM-4.5, this generation brings several key improvements: Longer context window: The context window has been expanded from 128K to 200K tokens, enabling the model to handle more complex...

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.