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InclusionAI

Ling-3.0-flash-Fin

  • Released
  • Open weights
  • MIT
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Intelligence Index

Reasoning setting

22.6#48 of 83measured models
Measured · Artificial Analysis v4.3.2
API price from
$0.06 in / $0.18 out / 1M tokOpenRouter · DeepInfra · FP4
Context window
262.14K tok

Performance profile

CodingRank #56 of 94
42.4%
SciCode

Capabilities

Context window

Modalities

Measured setting

Integration

Reasoning trade-offs

Source (opens in a new tab)
Intelligence Index v4.3.2
Reasoning
Intelligence Index
22.6
Evaluation time
3.3m

Pricing & access

API route
OpenRouter · DeepInfra · FP4API
Tier & contextStandard

USD per 1M tokens

Input
$0.06
Output
$0.18
Cached input
$0.012

Run locally

GGUF
Context
Estimated memory

~79.8 GiB

RAM + VRAM

Estimate assumptions

One text chat at the selected context, with a 16-bit cache and app allowance. Operating system and image processing are extra; optimized caches can use less.

96 GiB RAMwithout GPU offload

Memory budgetOne text chat
Weights
72.5 GiB
Context + state
0.1 GiB16-bit cache
App allowance
7.2 GiB
Download
77.8 GB2 files · keep together
Disk space
~91 GB
Available software
Ollama (opens in a new tab)LM Studio (opens in a new tab)Jan (opens in a new tab)KoboldCpp (opens in a new tab)llama.cpp (opens in a new tab)
Get GGUF (opens in a new tab)
Set up in Ollama · Q4_K_M · 8K context

Save the following as Modelfile. The context matches the estimate above.

FROM hf.co/bartowski/Ling-3.0-flash-Fin-GGUF:Q4_K_M
PARAMETER num_ctx 8192

Then run these commands in your terminal:

ollama create llmcurrent-local -f Modelfile
ollama run llmcurrent-local

Ollama import instructions (opens in a new tab) · For LM Studio or Jan, open the app’s model search, enter bartowski/Ling-3.0-flash-Fin-GGUF, choose Q4_K_M, and set context to 8,192 tokens in the app’s model settings. Software links above open each app’s setup guide.

vLLM setup (opens in a new tab)

Benchmarks

Full leaderboardSource (opens in a new tab)

Humanity’s Last Exam

Frontier reasoning

Ling-3.0-flash-Fin · Reasoning#59 of 126

  1. 1Claude Opus 5.5Max61.4%
  2. 2Claude Fable 5.1Max59.1%
  3. 3Gemini 4 ArgonHigh57.1%
  5. 56Nemotron 3.5 Bolt 74B-A9BReasoning24.7%
  6. 57Gemma 4 31BReasoning23.6%
  7. 58K2 Horizon MoVA 36B A4BReasoning23.4%
  8. 59Ling-3.0-flash-FinReasoning22.6%
  9. 60Qwen3.6 35B A3BReasoning22.2%
  10. 61Muse GlimmerHigh22.0%
  11. 61Ling-3.0-flash-VLReasoning22.0%

Peers use their best measured result on this benchmark.

Alternatives

Each model at its highest-scoring measured setting · Intelligence Index v4.3.2 and its evaluation cost per task.