vs
1:0
overall capability wins (1 ties across 2 scored). Ling-3.0 Flash Fin leads overall.
- Specs1:0
Verdict
Ling-3.0 Flash Fin and Ling-3.0 Tiny are closely matched on the overlapping metrics we have.
Cost for 1M in + 250K out
Illustrative chat workload at primary-provider list prices.
- Ling-3.0 Flash Fin
- -
- Ling-3.0 Tiny
- -
Price per 1M tokens
Full comparison
Organization
InclusionAI
InclusionAI
Family
Other
Other
License
MIT
MIT
Open weights
Yes
Yes
Release
Sep 3, 2026
Aug 17, 2026
Knowledge cutoff
-
-
API / provider
No primary API price listed
No primary API price listed
Modalities
text → text
text → text
Specs
Context window
262K
262K
Max output
-
33K
Parameters
124B (5.1B act.)
7.9B (1.3B act.)
Pricing
Input $/1M
—
—
Output $/1M
—
—
Blended $/1M (3∶1)
-
-
Speed
tok/s
-
-
TTFT (s)
-
-
Reasoning
MMLU-Pro
—
—
GPQA Diamond
—
73.4
Humanity's Last Exam
—
9.3
AIME 2025
—
—
MATH-500
—
—
Humanity's Last Exam (with tools)
—
—
AA-Omniscience Accuracy
—
8.5
AA-LCR v1.1
—
60.3
CritPt
—
—
MMMU-Pro
—
—
IFBench
—
—
Chartography
—
—
Chartography (With Tools)
—
—
Coding
SWE-bench Verified
—
—
SWE-bench Pro
—
—
SWE-bench Multilingual
—
—
LiveCodeBench
—
—
Terminal-Bench 2.1
—
27.7
Aider Polyglot
—
—
Terminal-Bench 3
—
—
BigCodeBench
—
—
SciCode
—
24.2
CursorBench
—
—
SWE-Rebench
—
—
NL2Repo-Bench
—
—
DeepSWE
—
—
WebDev Arena
—
—
Terminal-Bench 4.0
—
—
CursorBench 4.0
—
—
FrontierCode v1.1 (Main)
—
—
Arena
LMArena Elo
—
—
| Metric | Ling-3.0 Flash Fin | Ling-3.0 Tiny | Delta |
|---|---|---|---|
| Identity | |||
| Organization | InclusionAI | InclusionAI | — |
| Family | Other | Other | — |
| License | MIT | MIT | — |
| Open weights | Yes | Yes | — |
| Release | Sep 3, 2026 | Aug 17, 2026 | — |
| Knowledge cutoff | - | - | — |
| API / provider | No primary API price listed | No primary API price listed | — |
| Modalities | text → text | text → text | — |
| Specs | |||
| Context window | 262K | 262K | tie |
| Max output | - | 33K | — |
| Parameters | 124B (5.1B act.) | 7.9B (1.3B act.) | A +116 |
| Pricing | |||
| Input $/1M | — | — | — |
| Output $/1M | — | — | — |
| Blended $/1M (3∶1) | - | - | — |
| Speed | |||
| tok/s | - | - | — |
| TTFT (s) | - | - | — |
| Reasoning | |||
| MMLU-Pro | — | — | — |
| GPQA Diamond | — | 73.4 | — |
| Humanity's Last Exam | — | 9.3 | — |
| AIME 2025 | — | — | — |
| MATH-500 | — | — | — |
| Humanity's Last Exam (with tools) | — | — | — |
| AA-Omniscience Accuracy | — | 8.5 | — |
| AA-LCR v1.1 | — | 60.3 | — |
| CritPt | — | — | — |
| MMMU-Pro | — | — | — |
| IFBench | — | — | — |
| Chartography | — | — | — |
| Chartography (With Tools) | — | — | — |
| Coding | |||
| SWE-bench Verified | — | — | — |
| SWE-bench Pro | — | — | — |
| SWE-bench Multilingual | — | — | — |
| LiveCodeBench | — | — | — |
| Terminal-Bench 2.1 | — | 27.7 | — |
| Aider Polyglot | — | — | — |
| Terminal-Bench 3 | — | — | — |
| BigCodeBench | — | — | — |
| SciCode | — | 24.2 | — |
| CursorBench | — | — | — |
| SWE-Rebench | — | — | — |
| NL2Repo-Bench | — | — | — |
| DeepSWE | — | — | — |
| WebDev Arena | — | — | — |
| Terminal-Bench 4.0 | — | — | — |
| CursorBench 4.0 | — | — | — |
| FrontierCode v1.1 (Main) | — | — | — |
| Arena | |||
| LMArena Elo | — | — | — |
Ling-3.0 Flash Fin
- Context
- 262K
- Parameters
- 124B (5.1B act.)
- Price
- —
- Speed
- —
- Modalities
- text → text
- License
- MIT
InclusionAI / Ant Group's finance-specialized fine-tune of Ling-3.0 Flash (124B total / 5.1B active MoE, MIT, 256K context), built with financial institutions for source-grounded financial research, multi-document analysis, valuation modeling, and spreadsheet workflows. Ant Group's model card reports evaluation on FinFIRST, FinSearchComp Verified, FinCRAFT, Finance Agent, APEX-Agents, SpreadsheetBench, and τ³-Banking without publishing numeric scores, so no benchmark values are recorded here.
Ling-3.0 Tiny
- Context
- 262K / 33K out
- Parameters
- 7.9B (1.3B act.)
- Price
- —
- Speed
- —
- Modalities
- text → text
- License
- MIT
Compact on-device member of InclusionAI's Ling 3.0 family (7.9B total / 1.3B active MoE, MIT) with a hybrid KDA/MLA attention stack, native 256K context, and switchable thinking / instant modes.
Benchmark charts
Winner bars are emphasized. Per-benchmark deltas sit above each chart.
These two models have no overlapping published benchmarks in our dataset. Compare specs and pricing instead.