vs
1:1
overall capability wins (0 ties across 2 scored). Price/speed category wins do not decide overall. Tied overall.
- Specs1:1
Verdict
Ling-2.6 1T offers larger context (262K vs 256K); Atria Dawn Preview allows ~2.0x more max output (66K vs 33K).
- Ling-2.6 1T offers larger context (262K vs 256K)
- Atria Dawn Preview allows ~2.0x more max output (66K vs 33K)
Cost for 1M in + 250K out
Illustrative chat workload at primary-provider list prices.
- Atria Dawn Preview
- -
- Ling-2.6 1T
- $0.23
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
Mar 10, 2026
Knowledge cutoff
-
-
API / provider
No primary API price listed
InclusionAI
Modalities
text → text
text → text
Specs
Context window
256K
262K
Max output
66K
33K
Parameters
744B
—
Pricing
Input $/1M
—
$0.07
Output $/1M
—
$0.63
Blended $/1M (3∶1)
-
$0.21
Speed
tok/s
-
55
TTFT (s)
-
0.55
Reasoning
MMLU-Pro
—
—
GPQA Diamond
—
75.2
Humanity's Last Exam
—
8.7
AIME 2025
—
—
MATH-500
—
—
Humanity's Last Exam (with tools)
—
—
AA-Omniscience Accuracy
—
21.9
AA-LCR v1.1
—
41.7
CritPt
—
0.3
MMMU-Pro
—
—
IFBench
—
56.9
Chartography
—
—
Chartography (With Tools)
—
—
Coding
SWE-bench Verified
—
—
SWE-bench Pro
59.6
—
SWE-bench Multilingual
—
—
LiveCodeBench
—
—
Terminal-Bench 2.1
—
—
Aider Polyglot
—
—
Terminal-Bench 3
—
—
BigCodeBench
—
—
SciCode
—
37.0
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-2.6 1T | Delta |
|---|---|---|---|
| Identity | |||
| Organization | Shanghai AI Lab | InclusionAI | — |
| Family | Other | Other | — |
| License | MIT | MIT | — |
| Open weights | Yes | Yes | — |
| Release | Sep 11, 2026 | Mar 10, 2026 | — |
| Knowledge cutoff | - | - | — |
| API / provider | No primary API price listed | InclusionAI | — |
| Modalities | text → text | text → text | — |
| Specs | |||
| Context window | 256K | 262K | B +6K |
| Max output | 66K | 33K | A +33K |
| Parameters | 744B | — | — |
| Pricing | |||
| Input $/1M | — | $0.07 | — |
| Output $/1M | — | $0.63 | — |
| Blended $/1M (3∶1) | - | $0.21 | — |
| Speed | |||
| tok/s | - | 55 | — |
| TTFT (s) | - | 0.55 | — |
| Reasoning | |||
| MMLU-Pro | — | — | — |
| GPQA Diamond | — | 75.2 | — |
| Humanity's Last Exam | — | 8.7 | — |
| AIME 2025 | — | — | — |
| MATH-500 | — | — | — |
| Humanity's Last Exam (with tools) | — | — | — |
| AA-Omniscience Accuracy | — | 21.9 | — |
| AA-LCR v1.1 | — | 41.7 | — |
| CritPt | — | 0.3 | — |
| MMMU-Pro | — | — | — |
| IFBench | — | 56.9 | — |
| Chartography | — | — | — |
| Chartography (With Tools) | — | — | — |
| Coding | |||
| SWE-bench Verified | — | — | — |
| SWE-bench Pro | 59.6 | — | — |
| SWE-bench Multilingual | — | — | — |
| LiveCodeBench | — | — | — |
| Terminal-Bench 2.1 | — | — | — |
| Aider Polyglot | — | — | — |
| Terminal-Bench 3 | — | — | — |
| BigCodeBench | — | — | — |
| SciCode | — | 37.0 | — |
| 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-2.6 1T
- Context
- 262K / 33K out
- Parameters
- —
- Price
- $0.07 / $0.63
- Speed
- 55 tok/s · 0.55s TTFT
- Modalities
- text → text
- License
- MIT
InclusionAI trillion-scale flagship — stronger reasoning sibling to Ling Flash.
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.