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Ling 3.0 Flash Fin vs Step 3.7 Flash

Ling 3.0 Flash Fin leads the LLM Stats Score 43.0 to 30.2. Ling 3.0 Flash Fin is 4.9x cheaper per token.

InclusionAI · StepFun · Updated for 2026

Which is better?

Ling 3.0 Flash Fin leads the overall LLM Stats Score 43.0 to 30.2, ranking #44 overall.

On price, Ling 3.0 Flash Fin is roughly 4.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

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

Choose Ling 3.0 Flash Fin

  • overall performance matters — it scores 43.0 and ranks #44 on LLM Stats
  • your work emphasizes reasoning and agents — it leads those capability indexes
  • cost matters — it's about 4.9x cheaper per token
  • you want the most recent training data — it shipped Sep 2026

Choose Step 3.7 Flash

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

At a glance

The differences that matter most.

Ling 3.0 Flash Fin
InclusionAI
Step 3.7 Flash
StepFun
Core performance indexes
LLM Stats Score
43.0
#44
30.2
#120
Reasoning
44.2
#35
30.2
#117
Agents
29.2
#37
13.5
#92
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.06 / M
$0.20 / M
Output price
$0.18 / M
$1.15 / M
Context window
262,144
262,144

Individual benchmarks

6 reported for Ling 3.0 Flash Fin · 4 for Step 3.7 Flash

No common benchmarks found

Ling 3.0 Flash Fin and Step 3.7 Flashdon'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, Ling 3.0 Flash Fin ($0.06/1M tokens) is 3.3x cheaper than Step 3.7 Flash ($0.20/1M tokens).

For output processing, Ling 3.0 Flash Fin ($0.18/1M tokens) is 6.4x cheaper than Step 3.7 Flash ($1.15/1M tokens).

In conclusion, Step 3.7 Flash 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
Sun Sep 20 2026 • llm-stats.com
InclusionAI
Ling 3.0 Flash Fin
Input tokens$0.06
Output tokens$0.18
Best providerDeepinfra
StepFun
Step 3.7 Flash
Input tokens$0.20
Output tokens$1.15
Best providerDeepinfra
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

74.0B diff

Step 3.7 Flash has 74.0B more parameters than Ling 3.0 Flash Fin, making it 59.7% larger.

InclusionAI
Ling 3.0 Flash Fin
124.0Bparameters
StepFun
Step 3.7 Flash
198.0Bparameters
124.0B
Ling 3.0 Flash Fin
198.0B
Step 3.7 Flash

Context Window

Maximum input and output token capacity

Both models have the same input context window of 262,144 tokens. Both models can generate responses up to 262,144 tokens.

InclusionAI
Ling 3.0 Flash Fin
Input262,144 tokens
Output262,144 tokens
StepFun
Step 3.7 Flash
Input262,144 tokens
Output262,144 tokens
Sun Sep 20 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Step 3.7 Flash supports multimodal inputs, whereas Ling 3.0 Flash Fin does not.

Step 3.7 Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.

Ling 3.0 Flash Fin

Text
Images
Audio
Video

Step 3.7 Flash

Text
Images
Audio
Video

Release Timeline

When each model was launched

Ling 3.0 Flash Fin was released on 2026-09-03, while Step 3.7 Flash was released on 2026-06-10.

Ling 3.0 Flash Fin is 3 months newer than Step 3.7 Flash.

Ling 3.0 Flash Fin

Sep 3, 2026

2 weeks ago

2mo newer
Step 3.7 Flash

Jun 10, 2026

3 months 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

Ling 3.0 Flash Fin is available from DeepInfra. Step 3.7 Flash is available from DeepInfra.

Ling 3.0 Flash Fin

deepinfra logo
Deepinfra
Input Price:Input: $0.06/1MOutput Price:Output: $0.18/1M

Step 3.7 Flash

deepinfra logo
Deepinfra
Input Price:Input: $0.20/1MOutput Price:Output: $1.15/1M
* 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 Ling 3.0 Flash Fin and Step 3.7 Flash side-by-side, then vote on the output you prefer.

Ling 3.0 Flash Fin
✓ Preferred
Step 3.7 Flash
Open in Playground

FAQ

Common questions about Ling 3.0 Flash Fin vs Step 3.7 Flash.

Which is better, Ling 3.0 Flash Fin or Step 3.7 Flash?

Ling 3.0 Flash Fin leads the LLM Stats Score 43.0 to 30.2. Ling 3.0 Flash Fin is made by InclusionAI and Step 3.7 Flash is made by StepFun. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does Ling 3.0 Flash Fin compare to Step 3.7 Flash in benchmarks?

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%. Step 3.7 Flash scores Terminal-Bench 2.1: 59.5%, SWE-Bench Pro: 56.3%, Humanity's Last Exam (with tools, text-only): 47.2%, GDPval: 45.8%.

Is Ling 3.0 Flash Fin cheaper than Step 3.7 Flash?

Ling 3.0 Flash Fin is 3.3x cheaper for input tokens. Ling 3.0 Flash Fin costs $0.06/M input and $0.18/M output via deepinfra. Step 3.7 Flash costs $0.20/M input and $1.15/M output via deepinfra.

What are the context window sizes for Ling 3.0 Flash Fin and Step 3.7 Flash?

Ling 3.0 Flash Fin supports 262K tokens and Step 3.7 Flash 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 Ling 3.0 Flash Fin and Step 3.7 Flash?

Key differences include LLM Stats Score (43.0 vs 30.2), input pricing ($0.06 vs $0.20/M), multimodal support (no vs yes), licensing (Unknown vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes Ling 3.0 Flash Fin and Step 3.7 Flash?

Ling 3.0 Flash Fin is developed by InclusionAI and Step 3.7 Flash is developed by StepFun.

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