vs
2:1
overall capability wins (0 ties across 3 scored). Atria Dawn Preview leads overall.
- Specs2:1
Verdict
InternLM2.5 20B offers ~3.9x larger context (1M vs 256K); Atria Dawn Preview allows ~8.0x more max output (66K vs 8K).
- InternLM2.5 20B offers ~3.9x larger context (1M vs 256K)
- Atria Dawn Preview allows ~8.0x more max output (66K vs 8K)
Cost for 1M in + 250K out
Illustrative chat workload at primary-provider list prices.
- Atria Dawn Preview
- -
- InternLM2.5 20B
- $0.38
Price per 1M tokens
Full comparison
Organization
Shanghai AI Lab
Shanghai AI Lab
Family
Other
Other
License
MIT
Apache 2.0
Open weights
Yes
Yes
Release
Sep 11, 2026
Aug 1, 2024
Knowledge cutoff
-
-
API / provider
No primary API price listed
Together / Fireworks (ref.)
Modalities
text → text
text → text
Specs
Context window
256K
1M
Max output
66K
8K
Parameters
744B
20B
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
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 | Atria Dawn Preview | InternLM2.5 20B | Delta |
|---|---|---|---|
| Identity | |||
| Organization | Shanghai AI Lab | Shanghai AI Lab | — |
| Family | Other | Other | — |
| License | MIT | Apache 2.0 | — |
| Open weights | Yes | Yes | — |
| Release | Sep 11, 2026 | Aug 1, 2024 | — |
| Knowledge cutoff | - | - | — |
| API / provider | No primary API price listed | Together / Fireworks (ref.) | — |
| Modalities | text → text | text → text | — |
| Specs | |||
| Context window | 256K | 1M | B +744K |
| Max output | 66K | 8K | A +57K |
| Parameters | 744B | 20B | A +724 |
| 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 | 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 | — | — | — |
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.
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.
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.