# OrcaSAQ 2 27B

URL: https://interfaze.ai/models/orcarouterorcasaq-2-27b

[All models](https://interfaze.ai/models)

OrcaSAQ 2 27B by orcarouter, a text-generation model. Understand and compare features, benchmarks, and capabilities.

## Comparison

| Feature | OrcaSAQ 2 27B | Interfaze |
| --- | --- | --- |
| Input Modalities | text | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | No | Yes |
| Language Support | 75 partial | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | Yes | Yes |
| Context Input Size | 262.1K | 1M |
| Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search |

### Scaling

| Feature | OrcaSAQ 2 27B | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)

View model card on [Hugging Face](https://huggingface.co/orcarouter/OrcaSAQ-2-27B)

* * *

> ## 27B reasoning. 12.3 GB.
> 
> **OrcaSAQ2 27B** compresses Qwen3.8-27B from a **54 GB BF16 checkpoint to 12.3 GB** while preserving extremely high fidelity to the original model.
> 
> Built for: **long-horizon agents · coding · tool use · reasoning · stateful execution**

OrcaSAQ2 is a proprietary **sensitivity-aware mixed-precision quantization system** developed by OrcaRouter and its research team behind.

It is optimized around one goal: **Preserve as much useful model behavior as possible inside a practical GPU memory envelope.**

The resulting checkpoint provides:

-   **77.2% smaller storage footprint**
-   only **+0.02% perplexity** versus BF16
-   **93.2% token-level Top-1 agreement**
-   **0.031 mean KLD**
-   **262K context**
-   thinking mode
-   tool calling
-   MTP speculative decoding
-   production serving through **vLLM**

* * *

## At a glance

| Metric | BF16 | OrcaSAQ2 |
| --- | --- | --- |
| Checkpoint | 54 GB | 12.3 GB |
| Relative size | 100% | 22.8% |
| Storage reduction | — | 77.2% |
| Decoder precision | 16-bit | 3.21 bpw avg. |
| Perplexity | 5.6468 | 5.6482 |
| PPL delta | — | +0.02% |
| Top-1 agreement | 100% | 93.2% |
| Mean KLD | — | 0.031 |
| Context | 262K | 262K |

* * *

## BF16 fidelity

All numbers below are measured using **these exact OrcaSAQ2 weights** against the BF16 reference through the same evaluation path.

### WikiText-2

**16,376 predicted tokens**

| Build | Size | Decoder Bits | Mean KLD ↓ | Top-1 Agreement ↑ | PPL ↓ |
| --- | --- | --- | --- | --- | --- |
| Qwen3.8-27B BF16 | 54 GB | 16 | — | 100% | 5.6468 |
| OrcaSAQ2 27B | 12.3 GB | 3.21 | 0.031 | 93.2% | 5.6482 |

### Perplexity

```
BF16       5.6468  ████████████████████████████████████████
OrcaSAQ2   5.6482  ████████████████████████████████████████

Delta: +0.02%
```

### Top-1 agreement

```
OrcaSAQ2 vs BF16

███████████████████████████████████████████████░░░  93.2%
```

### Model footprint

```
Qwen3.8-27B BF16

██████████████████████████████████████████████████  54.0 GB

OrcaSAQ2

███████████                                         12.3 GB
```

**77.2% smaller.**

* * *

## Long-horizon agents

### Low-bit fidelity matters more as the horizon grows.

Short benchmarks can hide small degradation.

Agents cannot.

A small model error can change a tool call.

That changes the environment state.

The changed state affects every decision that follows.

```
Plan
  ↓
Act
  ↓
Observe
  ↓
Decide
  ↓
Recover
  ↓
Repeat
  ↓
...
  ↓
Task Success
```

Across long trajectories, small errors can compound into large behavioral differences.

That makes long-horizon execution an especially useful stress test for compressed reasoning models.

OrcaSAQ2 performs **strongly on long-horizon workloads relative to models in its deployment and parameter class**, despite operating from a **12.3 GB checkpoint**.

This makes it particularly suitable for:

-   coding agents
-   terminal agents
-   browser agents
-   computer-use agents
-   security agents
-   repository-scale tasks
-   multi-tool workflows
-   failure recovery
-   long-running stateful execution

### Why this matters

Perplexity asks:

> **How similar is the next-token distribution?**

Long-horizon evaluation asks:

> **Can the model still finish the job after many decisions?**

For agent models, both matter.

* * *

## Long-horizon performance

Agent benchmarks depend heavily on the surrounding scaffold, tools, reasoning budget, timeouts and execution environment. The results below are therefore shown as **public reference points, not direct apples-to-apples comparisons**.

#### SWE-bench Verified

| Model | Reported score |
| --- | --- |
| Claude Sonnet 4.6 | 79.6 |
| Claude Sonnet 4.5 | 77.2 |
| Gemini 3 | 76.2 |
| OrcaSAQ2 27B | 70.0 |
| Qwen3-Coder-480B-A35B | 69.6 |
| Gemini 2.5 Pro | 63.8 |
| GPT-4.1 | 54.6 |

**70.0% SWE-bench Verified from a 12.06 GB 27B checkpoint.**

#### Terminal-Bench 2.1

| Model / Agent | Reported score |
| --- | --- |
| Gemini 3.1 Pro / Terminus 2 | 70.7 |
| Claude Opus 4.6 / Claude Code | 70.1 |
| Claude Opus 4.6 / Terminus 2 | 63.8 |
| Claude Sonnet 4.6 / Claude Code | 58.5 |
| OrcaSAQ2 27B | 58.4 |
| Gemini 3 Flash / Gemini CLI | 56.9 |
| GPT-5.4 / Terminus 2 | 54.8 |
| Claude Sonnet 4.6 / Terminus 2 | 51.5 |

**58.4% Terminal-Bench 2.1 while fitting in ~12 GB of checkpoint storage.**

> Public scores use different agent stacks and should not be interpreted as a strict model-only ranking.

* * *

## Architecture

|  |  |
| --- | --- |
| Base model | Qwen/Qwen3.8-27B |
| Architecture | Qwen3\_5ForCausalLM |
| Layers | 64 |
| Hidden size | 5120 |
| Hybrid attention | 48 Gated DeltaNet + 16 full-attention layers |
| Context | 262,144 tokens |
| Vocabulary | 248,320 |
| MTP head | Included |
| Thinking | Supported |
| Tool calling | Supported |
| Checkpoint | 12.3 GB |
| Decoder average | 3.21 bpw |
| Serving | vLLM |
| Vision | Not included |
| License | Apache-2.0 |

* * *

## Production serving

### Up to 90.1 tok/s single-stream on a 16 GB GPU

Measured under a **15.7 GiB GPU memory cap**.

| Configuration | 1 Stream | 8 Streams | 16 Streams | KV Pool |
| --- | --- | --- | --- | --- |
| vLLM · MTP off | 65.3 tok/s | 332 tok/s | 333 tok/s | 29,354 tok |
| vLLM · MTP on | 90.1 tok/s | 220 tok/s | 219 tok/s | 14,563 tok |

### MTP speculative decoding

```
Single-stream decode

MTP off    █████████████████████████████       65.3 tok/s

MTP on     ████████████████████████████████████████
                                                90.1 tok/s
```

**+38% single-stream decode throughput**

MTP trades additional compute and KV capacity for stronger interactive decode performance.

It is particularly useful for:

-   coding assistants
-   interactive agents
-   terminal agents
-   tool-heavy applications
-   low-concurrency inference

For highly batched workloads, benchmark both configurations.

* * *

## 27B on a 16 GB GPU

OrcaSAQ2's checkpoint is **12.3 GB**.

That makes deployment possible on hardware that cannot hold the original 54 GB BF16 checkpoint.

```
16 GB GPU
┌───────────────────────────────────────────┐
│                                           │
│   OrcaSAQ2 weights          12.3 GB       │
│   ███████████████████████████████████     │
│                                           │
│   Remaining               ~3.7 GB         │
│   ██████████                              │
│                                           │
└───────────────────────────────────────────┘
```

Actual usable memory depends on:

-   vLLM overhead
-   KV-cache configuration
-   MTP
-   batch size
-   context length
-   CUDA graph configuration

A practical starting point for a 16 GB GPU is approximately **32K interactive context**, then tune based on the workload.

The model architecture supports up to **262K context**.

* * *

## Built for agents

### Long-horizon execution

```
plan → act → observe → recover → repeat
```

### Coding

Repository-scale generation, editing, testing and debugging.

### Tool use

Structured workflows where action-selection quality matters.

### Reasoning

Preserving the capabilities of the 27B base model under an aggressive deployment constraint.

### Single-GPU deployment

A **12.3 GB** checkpoint designed around practical inference hardware.

### Production serving

**vLLM + MTP + OpenAI-compatible APIs.**

* * *

## Generation samples

One prompt each, first attempt.

### Pelican on a bicycle

The standard SVG test, asked for as an animation.

Chain over the chainring, cranks 180° out of phase, parallax background. Pure SMIL, no JavaScript. Used as generated.

### Low-poly Statue of Liberty

> Create a html low-poly 3D models of the Statue of Liberty

A single self-contained HTML file: Three.js scene, orbit controls, procedural geometry.

* * *

## Quickstart

### Install

```
pip install -U vllm huggingface_hub

pip install git+https://github.com/Continuum-AI-Corp/OrcaSAQ2-kernel
```

* * *

### Download

```
hf download orcarouter/OrcaSAQ2-27B \
  --local-dir ./OrcaSAQ2-27B
```

* * *

### Serve with vLLM

```
vllm serve ./OrcaSAQ2-27B \
  --served-model-name OrcaSAQ2-27B \
  --max-model-len 262144 \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_xml \
  --speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":2}'
```

* * *

## OpenAI-compatible API

```
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="not-needed",
)

response = client.chat.completions.create(
    model="OrcaSAQ2-27B",
    messages=[
        {
            "role": "user",
            "content": "Analyze this repository and plan the next five actions."
        }
    ],
)

print(response.choices[0].message.content)
```

* * *

## Recommended sampling

```
temperature = 1.0
top_p       = 0.95
top_k       = 20
```

Thinking mode is enabled by default.

For agent deployments, benchmark against the actual tool schema, context distribution and reasoning budget used in production.

* * *

## Evaluation philosophy

A low-bit reasoning model should not be judged by checkpoint size alone.

We look at the intersection of:

A useful low-bit model must remain useful after compression.

* * *

## Why perplexity alone is not enough

Perplexity is useful and reproducible.

It is not a complete measure of agentic capability.

Quantization can affect:

```
reasoning
   ↓
planning
   ↓
tool selection
   ↓
state tracking
   ↓
recovery
   ↓
task completion
```

That is why OrcaSAQ2 reports BF16 fidelity metrics alongside downstream and long-horizon evaluation.

* * *

## Method

OrcaSAQ2 uses a proprietary **sensitivity-aware mixed-precision quantization system** developed by OrcaRouter.

The implementation is optimized to preserve model quality under a strict deployment-memory target.

Detailed quantization methodology, calibration strategy, precision allocation and packing techniques are not currently disclosed.

* * *

## Limitations

-   OrcaSAQ2 inherits the capabilities, biases and limitations of Qwen3.8-27B.
-   Quantization is not mathematically lossless.
-   **93.2% Top-1 agreement** means some token decisions differ from BF16.
-   **+0.02% PPL** is a model-fidelity measurement and does not guarantee identical downstream performance.
-   Long-horizon comparisons should use a controlled same-harness evaluation.
-   This checkpoint is **text-only**.
-   The vision tower is not included.
-   OrcaSAQ2 requires the OrcaSAQ2 vLLM integration.
-   Maximum architectural context does not imply that the full context fits into every GPU memory envelope.

* * *

## Open source from OrcaRouter

### [OrcaCode Review](https://github.com/Continuum-AI-Corp/Orca-Code-Review)

Open multi-model code review.

### [OrcaReplay](https://github.com/Continuum-AI-Corp/OrcaReplay)

Record, replay, fork and debug AI-agent runs.

### [OrcaRouter Lite](https://github.com/Continuum-AI-Corp/OrcaRouter-Lite)

Self-hosted multi-model AI infrastructure.

* * *

## Citation

```
@misc{qwen38,
    title = {Qwen3.8-Max: A New Bar for Coding and Cowork},
    author = {{Qwen Team}},
    year = {2026},
    month = {August},
    url = {https://qwen.ai/blog?id=qwen3.8}
}
```

* * *

## License

**Apache-2.0**

Inherited from:

[`Qwen/Qwen3.8-27B`](https://huggingface.co/Qwen/Qwen3.8-27B)

Quantization does not change the underlying license obligations.

* * *

## Want more deterministic results?

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)