DeepSeek-R1-0528 vs Ling 3.0 Flash Fin
Ling 3.0 Flash Fin leads the LLM Stats Score 43.3 to 24.2. Ling 3.0 Flash Fin is 10.1x cheaper per token.
DeepSeek · InclusionAI · Updated for 2026
Which is better?
Ling 3.0 Flash Fin leads the overall LLM Stats Score 43.3 to 24.2, ranking #39 overall.
On price, Ling 3.0 Flash Fin is roughly 10.1x 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 DeepSeek-R1-0528
- 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 10.1x 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.
Individual benchmarks
16 reported for DeepSeek-R1-0528 · 6 for Ling 3.0 Flash Fin
DeepSeek-R1-0528 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
For input processing, DeepSeek-R1-0528 ($0.50/1M tokens) is 8.3x more expensive than Ling 3.0 Flash Fin ($0.06/1M tokens).
For output processing, DeepSeek-R1-0528 ($2.15/1M tokens) is 11.9x more expensive than Ling 3.0 Flash Fin ($0.18/1M tokens).
In conclusion, DeepSeek-R1-0528 is more expensive than Ling 3.0 Flash Fin.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-R1-0528 has 547.0B more parameters than Ling 3.0 Flash Fin, making it 441.1% larger.
Context Window
Maximum input and output token capacity
Ling 3.0 Flash Fin accepts 262,144 input tokens compared to DeepSeek-R1-0528's 163,840 tokens. Ling 3.0 Flash Fin can generate longer responses up to 262,144 tokens, while DeepSeek-R1-0528 is limited to 163,840 tokens.
Release Timeline
When each model was launched
DeepSeek-R1-0528 was released on 2025-05-28, while Ling 3.0 Flash Fin was released on 2026-09-03.
Ling 3.0 Flash Fin is 15 months newer than DeepSeek-R1-0528.
May 28, 2025
1.3 years ago
Sep 3, 2026
1 weeks ago
1.3yr newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-R1-0528 is available from DeepInfra, DeepSeek, Novita. Ling 3.0 Flash Fin is available from DeepInfra.
DeepSeek-R1-0528
Ling 3.0 Flash Fin
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-R1-0528 and Ling 3.0 Flash Fin side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-R1-0528 vs Ling 3.0 Flash Fin.