Interfaze Beta
Interfaze · Proprietary · bench-align-v5
Capability shape
Seven axes from the ranking source. A missing axis is drawn as a gap.
Runtime service evidence
Measured values with the date they were observed. Nothing is inferred from a sibling model or a provider claim.
Time to first token: latency to the first answer chunk (Artificial Analysis, via BenchLM). Reasoning models include thinking time, so values can run to tens or hundreds of seconds.
Evidence key: ObservedUnavailable
| Measurement | Value | Observed | Last good | Evidence |
|---|---|---|---|---|
| Time to first token | Unavailable | Unavailable | Unavailable | |
| Throughput | Unavailable | Unavailable | Unavailable |
Regional or per-endpoint measurements appear only when the API supplies them; none are modelled here.
Endpoint and price matrix
Every published price component, including cache reads and writes.
| Component | USD | Evidence |
|---|---|---|
| Input / 1M tokens | $1.50 | |
| Output / 1M tokens | $3.50 | |
| Cache read / 1M tokens | Unavailable | |
| Cache write / 1M tokens | Unavailable | |
| Blended / 1M (75% input / 25% output) | $2.00 |
Workload-aware monthly cost example
10 conversations per day × 8 messages × 22 active days, 1200 input and 400 output tokens per message, no cache. This uses the same calculator as the cost simulator, so an unavailable applicable rate makes the example unavailable too.
Benchmark record
10 matched benchmark rows with their published value, unit, and provenance.
Knowledge
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| GPQA Graduate-Level Google-Proof Q&A | 89.9 | 448 questions | Graduate level | GPQA: A Graduate-Level Google-Proof Q&A Benchmark |
| GPQA-D GPQA Diamond | 89.9 | Graduate-level science questions | Graduate level | Trinity-Large-Thinking: Scaling an Open Source Frontier Agent |
| MMMLU MMMLU | 90.9 | Multilingual academic QA | Broad multilingual knowledge | MMMLU |
Coding
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| Spider 2.0-Lite Spider 2.0-Lite | 52.9 | Text-to-SQL queries | Enterprise text-to-SQL | Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows |
Instruction Following
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| SOB Value Acc Structured Output Benchmark Value Accuracy | 79.5 | Structured output extraction | Production structured-output reliability | Structured Output Benchmark Leaderboard |
Multimodal & Grounded
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| MMMU-Pro Massive Multi-discipline Multimodal Understanding Pro | 71.1 | Multimodal academic reasoning | Frontier multimodal | MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark |
| OCRBench V2 OCRBench V2 | 70.7 | Image OCR tasks | Native visual text understanding | OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning |
| olmOCR olmOCR-Bench | 85.7 | Layout-rich PDF understanding | Complex document processing | olmOCR-Bench |
| VoxPopuli WER VoxPopuli-Cleaned-AA Word Error Rate | 2.4 | Speech-to-text transcription | Audio speech recognition | VoxPopuli-Cleaned-AA |
| RefCOCO (avg) RefCOCO average | 82.1 | Referring-expression grounding | Fine-grained visual grounding | RefCOCO referring expression datasets |
Lifecycle and limitations log
Lifecycle events the source associates with this model, plus what this profile does not claim.
That is not evidence the model has no lifecycle plan — only that this source published none.