Ling 3.0 Flash Fin vs Qwen3-235B-A22B-Instruct-2507
Ling 3.0 Flash Fin leads the LLM Stats Score 43.3 to 24.3. Ling 3.0 Flash Fin is 2.3x cheaper per token.
InclusionAI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Ling 3.0 Flash Fin leads the overall LLM Stats Score 43.3 to 24.3, ranking #39 overall.
On price, Ling 3.0 Flash Fin is roughly 2.3x 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 43.3 and ranks #39 on LLM Stats
- your work emphasizes reasoning and agents — it leads those capability indexes
- cost matters — it's about 2.3x cheaper per token
- you want the most recent training data — it shipped Sep 2026
Choose Qwen3-235B-A22B-Instruct-2507
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
6 reported for Ling 3.0 Flash Fin · 25 for Qwen3-235B-A22B-Instruct-2507
Ling 3.0 Flash Fin and Qwen3-235B-A22B-Instruct-2507don'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, Ling 3.0 Flash Fin ($0.06/1M tokens) is 1.5x cheaper than Qwen3-235B-A22B-Instruct-2507 ($0.09/1M tokens).
For output processing, Ling 3.0 Flash Fin ($0.18/1M tokens) is 3.1x cheaper than Qwen3-235B-A22B-Instruct-2507 ($0.55/1M tokens).
In conclusion, Qwen3-235B-A22B-Instruct-2507 is more expensive than Ling 3.0 Flash Fin.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen3-235B-A22B-Instruct-2507 has 111.0B more parameters than Ling 3.0 Flash Fin, making it 89.5% larger.
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.
Release Timeline
When each model was launched
Ling 3.0 Flash Fin was released on 2026-09-03, while Qwen3-235B-A22B-Instruct-2507 was released on 2025-07-22.
Ling 3.0 Flash Fin is 14 months newer than Qwen3-235B-A22B-Instruct-2507.
Sep 3, 2026
5 days ago
1.1yr newerJul 22, 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.
Provider Availability
Ling 3.0 Flash Fin is available from DeepInfra. Qwen3-235B-A22B-Instruct-2507 is available from DeepInfra, Fireworks, Novita.
Ling 3.0 Flash Fin
Qwen3-235B-A22B-Instruct-2507
Outputs Comparison
Judge for yourself.
Run your own prompts against Ling 3.0 Flash Fin and Qwen3-235B-A22B-Instruct-2507 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Ling 3.0 Flash Fin vs Qwen3-235B-A22B-Instruct-2507.