vs
3:1
overall capability wins (0 ties across 4 scored). Atria Dawn Preview leads overall.
- Coding1:0
- Tool use1:0
- Specs1:1
Verdict
Atria Dawn Preview leads coding (widest gap: +3.0 on SWE-Pro); Ling-3.0 Flash offers larger context (262K vs 256K).
- Atria Dawn Preview leads coding (widest gap: +3.0 on SWE-Pro)
- Ling-3.0 Flash offers larger context (262K vs 256K)
Cost for 1M in + 250K out
Illustrative chat workload at primary-provider list prices.
- Atria Dawn Preview
- -
- Ling-3.0 Flash
- -
Price per 1M tokens
Full comparison
Organization
Shanghai AI Lab
InclusionAI
Family
Other
Other
License
MIT
MIT
Open weights
Yes
Yes
Release
Sep 11, 2026
Aug 4, 2026
Knowledge cutoff
-
-
API / provider
No primary API price listed
No primary API price listed
Modalities
text → text
text → text
Specs
Context window
256K
262K
Max output
66K
-
Parameters
744B
124B (5.1B act.)
Pricing
Input $/1M
—
—
Output $/1M
—
—
Blended $/1M (3∶1)
-
-
Speed
tok/s
-
-
TTFT (s)
-
-
Reasoning
MMLU-Pro
—
—
GPQA Diamond
—
85.5
Humanity's Last Exam
—
23.7
AIME 2025
—
—
MATH-500
—
—
Humanity's Last Exam (with tools)
—
—
AA-Omniscience Accuracy
—
18.2
AA-LCR v1.1
—
73.0
CritPt
—
1.7
MMMU-Pro
—
—
IFBench
—
—
Chartography
—
—
Chartography (With Tools)
—
—
Coding
SWE-bench Verified
—
—
SWE-bench Pro
59.6
56.6
SWE-bench Multilingual
—
72.4
LiveCodeBench
—
—
Terminal-Bench 2.1
—
55.4
Aider Polyglot
—
—
Terminal-Bench 3
—
—
BigCodeBench
—
—
SciCode
—
42.0
CursorBench
—
—
SWE-Rebench
—
—
NL2Repo-Bench
—
—
DeepSWE
—
—
WebDev Arena
—
—
Terminal-Bench 4.0
—
—
CursorBench 4.0
—
—
FrontierCode v1.1 (Main)
—
—
Arena
LMArena Elo
—
—
| Metric | Atria Dawn Preview | Ling-3.0 Flash | Delta |
|---|---|---|---|
| Identity | |||
| Organization | Shanghai AI Lab | InclusionAI | — |
| Family | Other | Other | — |
| License | MIT | MIT | — |
| Open weights | Yes | Yes | — |
| Release | Sep 11, 2026 | Aug 4, 2026 | — |
| Knowledge cutoff | - | - | — |
| API / provider | No primary API price listed | No primary API price listed | — |
| Modalities | text → text | text → text | — |
| Specs | |||
| Context window | 256K | 262K | B +6K |
| Max output | 66K | - | — |
| Parameters | 744B | 124B (5.1B act.) | A +620 |
| Pricing | |||
| Input $/1M | — | — | — |
| Output $/1M | — | — | — |
| Blended $/1M (3∶1) | - | - | — |
| Speed | |||
| tok/s | - | - | — |
| TTFT (s) | - | - | — |
| Reasoning | |||
| MMLU-Pro | — | — | — |
| GPQA Diamond | — | 85.5 | — |
| Humanity's Last Exam | — | 23.7 | — |
| AIME 2025 | — | — | — |
| MATH-500 | — | — | — |
| Humanity's Last Exam (with tools) | — | — | — |
| AA-Omniscience Accuracy | — | 18.2 | — |
| AA-LCR v1.1 | — | 73.0 | — |
| CritPt | — | 1.7 | — |
| MMMU-Pro | — | — | — |
| IFBench | — | — | — |
| Chartography | — | — | — |
| Chartography (With Tools) | — | — | — |
| Coding | |||
| SWE-bench Verified | — | — | — |
| SWE-bench Pro | 59.6 | 56.6 | A +3.0 pts |
| SWE-bench Multilingual | — | 72.4 | — |
| LiveCodeBench | — | — | — |
| Terminal-Bench 2.1 | — | 55.4 | — |
| Aider Polyglot | — | — | — |
| Terminal-Bench 3 | — | — | — |
| BigCodeBench | — | — | — |
| SciCode | — | 42.0 | — |
| CursorBench | — | — | — |
| SWE-Rebench | — | — | — |
| NL2Repo-Bench | — | — | — |
| DeepSWE | — | — | — |
| WebDev Arena | — | — | — |
| Terminal-Bench 4.0 | — | — | — |
| CursorBench 4.0 | — | — | — |
| FrontierCode v1.1 (Main) | — | — | — |
| Arena | |||
| LMArena Elo | — | — | — |
Atria Dawn Preview
- Context
- 256K / 66K out
- Parameters
- 744B
- Price
- —
- Speed
- —
- Modalities
- text → text
- License
- MIT
Shanghai AI Laboratory's open-weight agentic MoE (744B total, MIT license, 256K context), built on a GLM-5.2 base and trained by the cross-institutional ATRIA initiative (with Fudan NLP Lab) for long-horizon research and engineering workflows: problem analysis, tool use, code, experiments, and failure recovery. Benchmark scores are from the model's own technical report and have not yet been independently verified by a third-party evaluator.
Ling-3.0 Flash
- Context
- 262K
- Parameters
- 124B (5.1B act.)
- Price
- —
- Speed
- —
- Modalities
- text → text
- License
- MIT
InclusionAI / Ant Ling hybrid-linear MoE (124B total / 5.1B active, MIT) with native 256K context, default thinking mode, and open BF16/FP8 weights. Official Hugging Face eval results include SWE-bench Pro 56.6, SWE-bench Multilingual 72.4, and HLE 22.7.
Benchmark charts
Winner bars are emphasized. Per-benchmark deltas sit above each chart.