vs
2:0
overall capability wins (0 ties across 2 scored). Agnes-3.0-Flash Preview leads overall.
- Specs2:0
Verdict
Agnes-3.0-Flash Preview offers ~2.0x larger context (262K vs 131K).
Cost for 1M in + 250K out
Illustrative chat workload at primary-provider list prices.
- Agnes-3.0-Flash Preview
- -
- Trinity Mini
- $0.08
Price per 1M tokens
Full comparison
Organization
Agnes AI
Arcee AI
Family
Other
Other
License
Apache 2.0
Apache 2.0
Open weights
Yes
Yes
Release
Sep 13, 2026
Nov 4, 2025
Knowledge cutoff
-
-
API / provider
No primary API price listed
Arcee AI
Modalities
text, image, video → text
text → text
Specs
Context window
262K
131K
Max output
-
8K
Parameters
33B
8B
Pricing
Input $/1M
—
$0.04
Output $/1M
—
$0.15
Blended $/1M (3∶1)
-
$0.07
Speed
tok/s
-
150
TTFT (s)
-
0.2
Reasoning
MMLU-Pro
—
—
GPQA Diamond
85.0
—
Humanity's Last Exam
—
—
AIME 2025
—
—
MATH-500
—
—
Humanity's Last Exam (with tools)
—
—
AA-Omniscience Accuracy
23.0
—
AA-LCR v1.1
68.3
—
CritPt
—
—
MMMU-Pro
—
—
IFBench
74.2
—
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
38.1
—
CursorBench
—
—
SWE-Rebench
—
—
NL2Repo-Bench
—
—
DeepSWE
—
—
WebDev Arena
—
—
Terminal-Bench 4.0
—
—
CursorBench 4.0
—
—
FrontierCode v1.1 (Main)
—
—
Arena
LMArena Elo
—
—
| Metric | Agnes-3.0-Flash Preview | Trinity Mini | Delta |
|---|---|---|---|
| Identity | |||
| Organization | Agnes AI | Arcee AI | — |
| Family | Other | Other | — |
| License | Apache 2.0 | Apache 2.0 | — |
| Open weights | Yes | Yes | — |
| Release | Sep 13, 2026 | Nov 4, 2025 | — |
| Knowledge cutoff | - | - | — |
| API / provider | No primary API price listed | Arcee AI | — |
| Modalities | text, image, video → text | text → text | — |
| Specs | |||
| Context window | 262K | 131K | A +131K |
| Max output | - | 8K | — |
| Parameters | 33B | 8B | A +25 |
| Pricing | |||
| Input $/1M | — | $0.04 | — |
| Output $/1M | — | $0.15 | — |
| Blended $/1M (3∶1) | - | $0.07 | — |
| Speed | |||
| tok/s | - | 150 | — |
| TTFT (s) | - | 0.2 | — |
| Reasoning | |||
| MMLU-Pro | — | — | — |
| GPQA Diamond | 85.0 | — | — |
| Humanity's Last Exam | — | — | — |
| AIME 2025 | — | — | — |
| MATH-500 | — | — | — |
| Humanity's Last Exam (with tools) | — | — | — |
| AA-Omniscience Accuracy | 23.0 | — | — |
| AA-LCR v1.1 | 68.3 | — | — |
| CritPt | — | — | — |
| MMMU-Pro | — | — | — |
| IFBench | 74.2 | — | — |
| 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 | 38.1 | — | — |
| CursorBench | — | — | — |
| SWE-Rebench | — | — | — |
| NL2Repo-Bench | — | — | — |
| DeepSWE | — | — | — |
| WebDev Arena | — | — | — |
| Terminal-Bench 4.0 | — | — | — |
| CursorBench 4.0 | — | — | — |
| FrontierCode v1.1 (Main) | — | — | — |
| Arena | |||
| LMArena Elo | — | — | — |
Agnes-3.0-Flash Preview
- Context
- 262K
- Parameters
- 33B
- Price
- —
- Speed
- —
- Modalities
- text, image, video → text
- License
- Apache 2.0
Agnes AI's (Sapiens AI, Singapore) first open-weight release: a 33B-parameter hybrid-attention model (54 gated delta-rule recurrent layers alternating with 18 global-attention layers, 3:1) under Apache 2.0, with a 262K-token context window and text/image/video input. This open-weight Preview checkpoint is distinct from Agnes AI's production "Agnes 3.0 Flash" API model, which runs a different checkpoint and is not open-weight; the two should not be conflated.
Trinity Mini
- Context
- 131K / 8K out
- Parameters
- 8B
- Price
- $0.04 / $0.15
- Speed
- 150 tok/s · 0.2s TTFT
- Modalities
- text → text
- License
- Apache 2.0
Arcee's compact open instruct model tuned for enterprise merge-and-serve workflows.
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.