vs
0:2
overall capability wins (0 ties across 2 scored). Atria Dawn Preview leads overall.
- Specs0:2
Verdict
Atria Dawn Preview offers ~3.9x larger context (256K vs 66K).
Cost for 1M in + 250K out
Illustrative chat workload at primary-provider list prices.
- Apertus 70B Instruct
- $1.55
- Atria Dawn Preview
- -
Price per 1M tokens
Full comparison
Organization
Swiss AI Initiative
Shanghai AI Lab
Family
Other
Other
License
Apache 2.0
MIT
Open weights
Yes
Yes
Release
Sep 2, 2025
Sep 11, 2026
Knowledge cutoff
-
-
API / provider
Public AI
No primary API price listed
Modalities
text → text
text → text
Specs
Context window
66K
256K
Max output
-
66K
Parameters
70B
744B
Pricing
Input $/1M
$0.82
—
Output $/1M
$2.92
—
Blended $/1M (3∶1)
$1.34
-
Speed
tok/s
-
-
TTFT (s)
-
-
Reasoning
MMLU-Pro
—
—
GPQA Diamond
27.2
—
Humanity's Last Exam
5.5
—
AIME 2025
—
—
MATH-500
—
—
Humanity's Last Exam (with tools)
—
—
AA-Omniscience Accuracy
11.6
—
AA-LCR v1.1
—
—
CritPt
—
—
MMMU-Pro
—
—
IFBench
25.9
—
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
—
—
CursorBench
—
—
SWE-Rebench
—
—
NL2Repo-Bench
—
—
DeepSWE
—
—
WebDev Arena
—
—
Terminal-Bench 4.0
—
—
CursorBench 4.0
—
—
FrontierCode v1.1 (Main)
—
—
Arena
LMArena Elo
—
—
| Metric | Apertus 70B Instruct | Atria Dawn Preview | Delta |
|---|---|---|---|
| Identity | |||
| Organization | Swiss AI Initiative | Shanghai AI Lab | — |
| Family | Other | Other | — |
| License | Apache 2.0 | MIT | — |
| Open weights | Yes | Yes | — |
| Release | Sep 2, 2025 | Sep 11, 2026 | — |
| Knowledge cutoff | - | - | — |
| API / provider | Public AI | No primary API price listed | — |
| Modalities | text → text | text → text | — |
| Specs | |||
| Context window | 66K | 256K | B +190K |
| Max output | - | 66K | — |
| Parameters | 70B | 744B | B +674 |
| Pricing | |||
| Input $/1M | $0.82 | — | — |
| Output $/1M | $2.92 | — | — |
| Blended $/1M (3∶1) | $1.34 | - | — |
| Speed | |||
| tok/s | - | - | — |
| TTFT (s) | - | - | — |
| Reasoning | |||
| MMLU-Pro | — | — | — |
| GPQA Diamond | 27.2 | — | — |
| Humanity's Last Exam | 5.5 | — | — |
| AIME 2025 | — | — | — |
| MATH-500 | — | — | — |
| Humanity's Last Exam (with tools) | — | — | — |
| AA-Omniscience Accuracy | 11.6 | — | — |
| AA-LCR v1.1 | — | — | — |
| CritPt | — | — | — |
| MMMU-Pro | — | — | — |
| IFBench | 25.9 | — | — |
| 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 | — | — | — |
| CursorBench | — | — | — |
| SWE-Rebench | — | — | — |
| NL2Repo-Bench | — | — | — |
| DeepSWE | — | — | — |
| WebDev Arena | — | — | — |
| Terminal-Bench 4.0 | — | — | — |
| CursorBench 4.0 | — | — | — |
| FrontierCode v1.1 (Main) | — | — | — |
| Arena | |||
| LMArena Elo | — | — | — |
Apertus 70B Instruct
- Context
- 66K
- Parameters
- 70B
- Price
- $0.82 / $2.92
- Speed
- —
- Modalities
- text → text
- License
- Apache 2.0
Flagship of the Swiss AI Initiative's fully open Apertus family — Apache 2.0 weights, data, and training recipe with broad multilingual coverage including Swiss German and Romansh.
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.
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.