vs
1:1
overall capability wins (0 ties across 2 scored). Price/speed category wins do not decide overall. Tied overall.
- Specs1:1
Verdict
InternLM2.5 20B offers ~3.8x larger context (1M vs 262K).
Cost for 1M in + 250K out
Illustrative chat workload at primary-provider list prices.
- InternLM2.5 20B
- $0.38
- Ling-3.0 Flash Fin
- -
Price per 1M tokens
Full comparison
Organization
Shanghai AI Lab
InclusionAI
Family
Other
Other
License
Apache 2.0
MIT
Open weights
Yes
Yes
Release
Aug 1, 2024
Sep 3, 2026
Knowledge cutoff
-
-
API / provider
Together / Fireworks (ref.)
No primary API price listed
Modalities
text → text
text → text
Specs
Context window
1M
262K
Max output
8K
-
Parameters
20B
124B (5.1B act.)
Pricing
Input $/1M
$0.30
—
Output $/1M
$0.30
—
Blended $/1M (3∶1)
$0.30
-
Speed
tok/s
70
-
TTFT (s)
0.4
-
Reasoning
MMLU-Pro
—
—
GPQA Diamond
—
—
Humanity's Last Exam
—
—
AIME 2025
—
—
MATH-500
—
—
Humanity's Last Exam (with tools)
—
—
AA-Omniscience Accuracy
—
—
AA-LCR v1.1
—
—
CritPt
—
—
MMMU-Pro
—
—
IFBench
—
—
Chartography
—
—
Chartography (With Tools)
—
—
Coding
SWE-bench Verified
—
—
SWE-bench Pro
—
—
SWE-bench Multilingual
—
—
LiveCodeBench
—
—
Terminal-Bench 2.1
—
—
Aider Polyglot
—
—
Terminal-Bench 3
—
—
BigCodeBench
—
—
SciCode
—
—
CursorBench
—
—
SWE-Rebench
—
—
NL2Repo-Bench
—
—
DeepSWE
—
—
WebDev Arena
—
—
Terminal-Bench 4.0
—
—
CursorBench 4.0
—
—
FrontierCode v1.1 (Main)
—
—
Arena
LMArena Elo
—
—
| Metric | InternLM2.5 20B | Ling-3.0 Flash Fin | Delta |
|---|---|---|---|
| Identity | |||
| Organization | Shanghai AI Lab | InclusionAI | — |
| Family | Other | Other | — |
| License | Apache 2.0 | MIT | — |
| Open weights | Yes | Yes | — |
| Release | Aug 1, 2024 | Sep 3, 2026 | — |
| Knowledge cutoff | - | - | — |
| API / provider | Together / Fireworks (ref.) | No primary API price listed | — |
| Modalities | text → text | text → text | — |
| Specs | |||
| Context window | 1M | 262K | A +738K |
| Max output | 8K | - | — |
| Parameters | 20B | 124B (5.1B act.) | B +104 |
| Pricing | |||
| Input $/1M | $0.30 | — | — |
| Output $/1M | $0.30 | — | — |
| Blended $/1M (3∶1) | $0.30 | - | — |
| Speed | |||
| tok/s | 70 | - | — |
| TTFT (s) | 0.4 | - | — |
| Reasoning | |||
| MMLU-Pro | — | — | — |
| GPQA Diamond | — | — | — |
| Humanity's Last Exam | — | — | — |
| AIME 2025 | — | — | — |
| MATH-500 | — | — | — |
| Humanity's Last Exam (with tools) | — | — | — |
| AA-Omniscience Accuracy | — | — | — |
| AA-LCR v1.1 | — | — | — |
| CritPt | — | — | — |
| MMMU-Pro | — | — | — |
| IFBench | — | — | — |
| Chartography | — | — | — |
| Chartography (With Tools) | — | — | — |
| Coding | |||
| SWE-bench Verified | — | — | — |
| SWE-bench Pro | — | — | — |
| SWE-bench Multilingual | — | — | — |
| LiveCodeBench | — | — | — |
| Terminal-Bench 2.1 | — | — | — |
| Aider Polyglot | — | — | — |
| Terminal-Bench 3 | — | — | — |
| BigCodeBench | — | — | — |
| SciCode | — | — | — |
| CursorBench | — | — | — |
| SWE-Rebench | — | — | — |
| NL2Repo-Bench | — | — | — |
| DeepSWE | — | — | — |
| WebDev Arena | — | — | — |
| Terminal-Bench 4.0 | — | — | — |
| CursorBench 4.0 | — | — | — |
| FrontierCode v1.1 (Main) | — | — | — |
| Arena | |||
| LMArena Elo | — | — | — |
InternLM2.5 20B
- Context
- 1M / 8K out
- Parameters
- 20B
- Price
- $0.30 / $0.30
- Speed
- 70 tok/s · 0.4s TTFT
- Modalities
- text → text
- License
- Apache 2.0
Open InternLM2.5 chat with up to 1M context — strong Chinese RAG and tool-use baseline.
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.
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.