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GLM-4.7 vs Ling 3.0 Flash Fin

Ling 3.0 Flash Fin leads the LLM Stats Score 43.3 to 34.4. Ling 3.0 Flash Fin is 8.2x cheaper per token.

Zhipu AI · InclusionAI · Updated for 2026

Which is better?

Ling 3.0 Flash Fin leads the overall LLM Stats Score 43.3 to 34.4, ranking #39 overall.

On price, Ling 3.0 Flash Fin is roughly 8.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

Ling 3.0 Flash Fin also accepts a larger context window (262,144 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

  • you need open weights you can self-host or fine-tune

Choose Ling 3.0 Flash Fin

  • overall performance matters — it scores 43.3 and ranks #39 on LLM Stats
  • your work emphasizes reasoning and agents — it leads those capability indexes
  • cost matters — it's about 8.2x cheaper per token
  • you process long inputs — it offers a 262,144 token context window
  • you want the most recent training data — it shipped Sep 2026

At a glance

The differences that matter most.

GLM-4.7
Zhipu AI
Ling 3.0 Flash Fin
InclusionAI
Core performance indexes
LLM Stats Score
34.4
#90
43.3
#39
Reasoning
34.3
#89
44.7
#32
Agents
9.6
#120
29.5
#35
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.40 / M
$0.06 / M
Output price
$1.75 / M
$0.18 / M
Context window
202,752
262,144

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
GLM-4.7
Ling 3.0 Flash Fin
Tool use
8.3#136
20.5#56
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

13 reported for GLM-4.7 · 6 for Ling 3.0 Flash Fin

No common benchmarks found

GLM-4.7 and Ling 3.0 Flash Findon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

Ling 3.0 Flash Fin costs less

For input processing, GLM-4.7 ($0.40/1M tokens) is 6.7x more expensive than Ling 3.0 Flash Fin ($0.06/1M tokens).

For output processing, GLM-4.7 ($1.75/1M tokens) is 9.7x more expensive than Ling 3.0 Flash Fin ($0.18/1M tokens).

In conclusion, GLM-4.7 is more expensive than Ling 3.0 Flash Fin.*

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

Lowest available price from all providers
Wed Sep 09 2026 • llm-stats.com
Zhipu AI
GLM-4.7
Input tokens$0.40
Output tokens$1.75
Best providerDeepinfra
InclusionAI
Ling 3.0 Flash Fin
Input tokens$0.06
Output tokens$0.18
Best providerDeepinfra
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

234.0B diff

GLM-4.7 has 234.0B more parameters than Ling 3.0 Flash Fin, making it 188.7% larger.

Zhipu AI
GLM-4.7
358.0Bparameters
InclusionAI
Ling 3.0 Flash Fin
124.0Bparameters
358.0B
GLM-4.7
124.0B
Ling 3.0 Flash Fin

Context Window

Maximum input and output token capacity

Ling 3.0 Flash Fin accepts 262,144 input tokens compared to GLM-4.7's 202,752 tokens. Ling 3.0 Flash Fin can generate longer responses up to 262,144 tokens, while GLM-4.7 is limited to 202,752 tokens.

Zhipu AI
GLM-4.7
Input202,752 tokens
Output202,752 tokens
InclusionAI
Ling 3.0 Flash Fin
Input262,144 tokens
Output262,144 tokens
Wed Sep 09 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

GLM-4.7 supports multimodal inputs, whereas Ling 3.0 Flash Fin does not.

GLM-4.7 can handle both text and other forms of data like images, making it suitable for multimodal applications.

GLM-4.7

Text
Images
Audio
Video

Ling 3.0 Flash Fin

Text
Images
Audio
Video

Release Timeline

When each model was launched

GLM-4.7 was released on 2025-12-22, while Ling 3.0 Flash Fin was released on 2026-09-03.

Ling 3.0 Flash Fin is 9 months newer than GLM-4.7.

GLM-4.7

Dec 22, 2025

8 months ago

Ling 3.0 Flash Fin

Sep 3, 2026

5 days ago

8mo newer

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 is available from DeepInfra, Fireworks, Novita. Ling 3.0 Flash Fin is available from DeepInfra.

GLM-4.7

deepinfra logo
Deepinfra
Input Price:Input: $0.40/1MOutput Price:Output: $1.75/1M
fireworks logo
Fireworks
Input Price:Input: $0.60/1MOutput Price:Output: $2.20/1M
novita logo
Novita
Input Price:Input: $0.60/1MOutput Price:Output: $2.20/1M

Ling 3.0 Flash Fin

deepinfra logo
Deepinfra
Input Price:Input: $0.06/1MOutput Price:Output: $0.18/1M
* 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 and Ling 3.0 Flash Fin side-by-side, then vote on the output you prefer.

GLM-4.7
✓ Preferred
Ling 3.0 Flash Fin
Open in Playground

FAQ

Common questions about GLM-4.7 vs Ling 3.0 Flash Fin.

Which is better, GLM-4.7 or Ling 3.0 Flash Fin?

Ling 3.0 Flash Fin leads the LLM Stats Score 43.3 to 34.4. GLM-4.7 is made by Zhipu AI and Ling 3.0 Flash Fin is made by InclusionAI. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does GLM-4.7 compare to Ling 3.0 Flash Fin in benchmarks?

GLM-4.7 scores AIME 2025: 95.7%, Tau-bench: 87.4%, GPQA: 85.7%, LiveCodeBench v6: 84.9%, MMLU-Pro: 84.3%. Ling 3.0 Flash Fin scores SpreadSheetBench-v1: 86.5%, Finance Agent v1.1: 69.2%, Finance Agent v2: 59.8%, Tau3 Banking: 41.0%, APEX-Agents: 29.2%.

Is GLM-4.7 cheaper than Ling 3.0 Flash Fin?

Ling 3.0 Flash Fin is 6.7x cheaper for input tokens. GLM-4.7 costs $0.40/M input and $1.75/M output via deepinfra. Ling 3.0 Flash Fin costs $0.06/M input and $0.18/M output via deepinfra.

What are the context window sizes for GLM-4.7 and Ling 3.0 Flash Fin?

GLM-4.7 supports 203K tokens and Ling 3.0 Flash Fin supports 262K 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 and Ling 3.0 Flash Fin?

Key differences include LLM Stats Score (34.4 vs 43.3), context window (203K vs 262K), input pricing ($0.40 vs $0.06/M), multimodal support (yes vs no), licensing (MIT vs Unknown). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-4.7 and Ling 3.0 Flash Fin?

GLM-4.7 is developed by Zhipu AI and Ling 3.0 Flash Fin is developed by InclusionAI.

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