GLM-4.7-Flash vs Kimi K2 Instruct

GLM-4.7-Flash and Kimi K2 Instruct are closely matched at 23.8 and 22.0 on the LLM Stats Score. GLM-4.7-Flash is 3.3x cheaper per token.

Zhipu AI · Moonshot AI · Updated for 2026

Which is better?

GLM-4.7-Flash and Kimi K2 Instruct are closely matched on the overall LLM Stats Score at 23.8 and 22.0.

In the 3 individual benchmarks reported for both models, GLM-4.7-Flash wins 3; this is a narrower head-to-head signal than the composite indexes.

On price, GLM-4.7-Flash is roughly 3.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

Kimi K2 Instruct also accepts a larger context window (200,000 input tokens), making it the stronger choice for long documents and large codebases.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose GLM-4.7-Flash

you value its reported benchmark strengths — it wins 3 of 3 exact shared results
cost matters — it's about 3.3x cheaper per token
you want the most recent training data — it shipped Jan 2026

Choose Kimi K2 Instruct

you process long inputs — it offers a 200,000 token context window

At a glance

The differences that matter most.

Better token value: GLM-4.7-Flash

Core performance indexes

LLM Stats Score

23.8

#163 · 6 evals

Best

22.0

#171 · 28 evals

Reasoning

23.8

#153 · 6 evals

Best

22.1

#163 · 22 evals

Coding

4.6

#199 · 1 evals

12.6

#138 · 4 evals

Best

Agents

4.9

#141 · 2 evals

Best

-0.9

#164 · 1 evals

Cost, coverage & limits

Benchmark wins

3 of 3

Best

0 of 3

Input price

$0.07 / M

Best

$0.50 / M

Output price

$0.40 / M

Best

$0.50 / M

Context window

128,000

200,000

Best

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared

Index

GLM-4.7-Flash

Kimi K2 Instruct

Math

21.2#142 · 2 evals

22.5#131 · 12 evals

Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

6 reported for GLM-4.7-Flash · 38 for Kimi K2 Instruct

3 shared

GLM-4.7-Flash outperforms in 3 benchmarks (AIME 2025, GPQA, Humanity's Last Exam), while Kimi K2 Instruct is better at 0 benchmarks.

GLM-4.7-Flash significantly outperforms across most benchmarks.

Fri Sep 04 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

GLM-4.7-Flash costs less

For input processing, GLM-4.7-Flash ($0.07/1M tokens) is 7.1x cheaper than Kimi K2 Instruct ($0.50/1M tokens).

For output processing, GLM-4.7-Flash ($0.40/1M tokens) is 1.3x cheaper than Kimi K2 Instruct ($0.50/1M tokens).

In conclusion, Kimi K2 Instruct is more expensive than GLM-4.7-Flash.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers

Fri Sep 04 2026 • llm-stats.com

GLM-4.7-Flash

Input tokens$0.07

Output tokens$0.40

Best providerUnknown Organization

Kimi K2 Instruct

Input tokens$0.50

Output tokens$0.50

Best providerFireworks

Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

970.0B diff

Kimi K2 Instruct has 970.0B more parameters than GLM-4.7-Flash, making it 3233.3% larger.

GLM-4.7-Flash

30.0Bparameters

Kimi K2 Instruct

1.0Tparameters

30.0B

GLM-4.7-Flash

1000.0B

Kimi K2 Instruct

Context Window

Maximum input and output token capacity

Kimi K2 Instruct accepts 200,000 input tokens compared to GLM-4.7-Flash's 128,000 tokens. Kimi K2 Instruct can generate longer responses up to 200,000 tokens, while GLM-4.7-Flash is limited to 16,384 tokens.

GLM-4.7-Flash

Input128,000 tokens

Output16,384 tokens

Kimi K2 Instruct

Input200,000 tokens

Output200,000 tokens

Fri Sep 04 2026 • llm-stats.com

License

Usage and distribution terms

Both models are licensed under MIT.

Both models share the same licensing terms, providing consistent usage rights.

GLM-4.7-Flash

MIT

Open weights

Kimi K2 Instruct

MIT

Open weights

Release Timeline

When each model was launched

GLM-4.7-Flash was released on 2026-01-19, while Kimi K2 Instruct was released on 2025-07-11.

GLM-4.7-Flash is 6 months newer than Kimi K2 Instruct.

GLM-4.7-Flash

Jan 19, 2026

7 months ago

6mo newer

Kimi K2 Instruct

Jul 11, 2025

1.1 years ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

GLM-4.7-Flash is available from ZAI. Kimi K2 Instruct is available from Fireworks, Novita.

GLM-4.7-Flash

Unknown Organization

Input Price:Input: $0.07/1MOutput Price:Output: $0.40/1M

Kimi K2 Instruct

Fireworks

Input Price:Input: $0.50/1MOutput Price:Output: $0.50/1M

Novita

Input Price:Input: $0.57/1MOutput Price:Output: $2.30/1M

* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against GLM-4.7-Flash and Kimi K2 Instruct side-by-side, then vote on the output you prefer.

GLM-4.7-Flash

✓ Preferred

Kimi K2 Instruct

Open in Playground

FAQ

Common questions about GLM-4.7-Flash vs Kimi K2 Instruct.

Which is better, GLM-4.7-Flash or Kimi K2 Instruct?

GLM-4.7-Flash and Kimi K2 Instruct are closely matched on the LLM Stats Score at 23.8 and 22.0. GLM-4.7-Flash is made by Zhipu AI and Kimi K2 Instruct is made by Moonshot AI. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does GLM-4.7-Flash compare to Kimi K2 Instruct in benchmarks?

GLM-4.7-Flash scores AIME 2025: 91.6%, Tau-bench: 79.5%, GPQA: 75.2%, SWE-Bench Verified: 59.2%, BrowseComp: 42.8%. Kimi K2 Instruct scores MATH-500: 97.4%, GSM8k: 97.3%, CBNSL: 95.6%, HumanEval: 93.3%, MMLU-Redux: 92.7%.

Is GLM-4.7-Flash cheaper than Kimi K2 Instruct?

GLM-4.7-Flash is 7.1x cheaper for input tokens. GLM-4.7-Flash costs $0.07/M input and $0.40/M output via z. Kimi K2 Instruct costs $0.50/M input and $0.50/M output via fireworks.

What are the context window sizes for GLM-4.7-Flash and Kimi K2 Instruct?

GLM-4.7-Flash supports 128K tokens and Kimi K2 Instruct supports 200K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between GLM-4.7-Flash and Kimi K2 Instruct?

Key differences include LLM Stats Score (23.8 vs 22.0), context window (128K vs 200K), input pricing ($0.07 vs $0.50/M). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-4.7-Flash and Kimi K2 Instruct?

GLM-4.7-Flash is developed by Zhipu AI and Kimi K2 Instruct is developed by Moonshot AI.

GLM-4.7-Flash vs Kimi K2 Instruct