DeepSWE
No official DeepSWE mini-swe-agent row for this identity on the public board we fetched.
Context window on the 2026-08-19 OpenRouter row: 128,000 tokens.
Max completion tokens were not listed on that row.
Input / output on that row: $0 / $0 per 1M tokens.
Architecture fields: text->text · Other.
Hugging Face id on that row: nvidia/NVIDIA-Nemotron-Nano-9B-v2.
NVIDIA-Nemotron-Nano-9B-v2 is a large language model (LLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks.
When you want the 2025-09-05 catalog SKU, not a later rename.
No official DeepSWE mini-swe-agent row for this identity on the public board we fetched.
We did not open a vals.ai card for this identity. Chip omitted.
Ranked view: Best coding models: the independent leaderboard puts this row and every other card that carries an independent coding score on one board, with the confidence intervals left visible.
Copied from AA or vals HTML we opened. 0.0% placeholder bars are omitted. Do not average these into the hero tiles, and do not invent CI, $/task, or steps for Artificial Analysis.
| Bench | Printed | Note | Source | URL | As of |
|---|---|---|---|---|---|
| SciCode | 20.9% | Printed on the AA Intelligence Evaluations grid for NVIDIA Nemotron Nano 9B V2 (Non-reasoning). Not a DeepSWE chip. | Artificial Analysis NVIDIA Nemotron Nano 9B V2 | source | 2026-08-19 |
| Humanity's Last Exam | 4.6% | Printed on the AA Intelligence Evaluations grid for NVIDIA Nemotron Nano 9B V2 (Non-reasoning). Not a DeepSWE chip. | Artificial Analysis NVIDIA Nemotron Nano 9B V2 | source | 2026-08-19 |
| GPQA Diamond | 55.7% | Printed on the AA Intelligence Evaluations grid for NVIDIA Nemotron Nano 9B V2 (Non-reasoning). Not a DeepSWE chip. | Artificial Analysis NVIDIA Nemotron Nano 9B V2 | source | 2026-08-19 |
| AA-Omniscience Accuracy | 9.8% | Printed on the AA Intelligence Evaluations grid for NVIDIA Nemotron Nano 9B V2 (Non-reasoning). Not a DeepSWE chip. | Artificial Analysis NVIDIA Nemotron Nano 9B V2 | source | 2026-08-19 |
| AA-LCR | 26% | Printed on the AA Intelligence Evaluations grid for NVIDIA Nemotron Nano 9B V2 (Non-reasoning). Not a DeepSWE chip. | Artificial Analysis NVIDIA Nemotron Nano 9B V2 | source | 2026-08-19 |
Plus is $25/mo with $25 weekly hosted usage. The Mac app stays free with your own keys. Get Plus is not Download for Mac.
Official weights id on the 2026-08-19 OpenRouter row: nvidia/NVIDIA-Nemotron-Nano-9B-v2. Community GGUF is a quant, not a second Continuum card.
This is the model repo, not Download for Mac. Downloads and likes from the Hugging Face API on 2026-08-19.
usedStorage and the sum of .safetensors sizes can differ (LFS pointers, non-shard files, duplicate copies). Cited separately. Source: recursive tree API, 2026-08-19.
| Path | Bytes |
|---|---|
model-00001-of-00004.safetensors | 4,924,823,528 |
model-00002-of-00004.safetensors | 4,937,507,160 |
model-00003-of-00004.safetensors | 4,871,563,216 |
model-00004-of-00004.safetensors | 3,042,598,608 |
tokenizer.json | 17,078,330 |
acc-vs-budget.png | 491,034 |
tokenizer_config.json | 181,326 |
accuracy_chart.png | 169,781 |
modeling_nemotron_h.py | 79,013 |
README.md | 47,338 |
model.safetensors.index.json | 26,843 |
nemotron_toolcall_parser_streaming.py | 21,296 |
configuration_nemotron_h.py | 12,176 |
nemotron_toolcall_parser_no_streaming.py | 3,723 |
explainability.md | 2,635 |
safety.md | 2,300 |
privacy.md | 2,297 |
bias.md | 2,277 |
.gitattributes | 1,679 |
config.json | 1,507 |
special_tokens_map.json | 422 |
generation_config.json | 158 |
All 22 files the tree API returned are listed: every one of the 4 safetensor shards plus 18 other files. Files tab: https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2/tree/main.
OpenRouter slug nvidia/nemotron-nano-9b-v2:free on the 2026-08-19 catalog.
First party: https://build.nvidia.com/.
Not on the Continuum host list fetched 2026-08-19.
We list first-party and OpenRouter, plus Continuum only when the live public allowlist named the id. This is not a 15-host routing table.
OpenRouter 2026-08-19: $0 in / $0 out per 1M tokens. Context 128,000 in.
No separate internal-reasoning price on that row.
| Claim | Source | As of |
|---|---|---|
| OpenRouter id nvidia/nemotron-nano-9b-v2:free; context 128000; created 1757106807; $0 / $0 per 1M | OpenRouter /api/v1/models | 2026-08-19 |
| AA Intelligence Index 7; 5 printed Index benches | Artificial Analysis model page | 2026-08-19 |
| OpenRouter id nvidia/nemotron-nano-9b-v2:free, context 128,000 | OpenRouter /api/v1/models | 2026-08-19 |
| HF downloads 380,464 | Hugging Face API nvidia/NVIDIA-Nemotron-Nano-9B-v2 | 2026-08-19 |
| HF safetensors.total: 8,888,227,328 stored tensors; HF API usedStorage 17,794,621,091 bytes; created 2025-08-12T22:43:32.000Z; HF tensors BF16 | Hugging Face API nvidia/NVIDIA-Nemotron-Nano-9B-v2 | 2026-08-19 |
| 4 safetensor shards; shard bytes 17,776,492,512; tree files 22 | Hugging Face tree API nvidia/NVIDIA-Nemotron-Nano-9B-v2 | 2026-08-19 |
No other card in this cluster shares a published DeepSWE official row we can put next to this one. We will not compare on vendor-blog numbers.
Continue through NVIDIA's model family with Nemotron 3 Ultra.
Lab posts pick a harness, an effort, a split, and sometimes a private eval set. DeepSWE publishes the official mini-swe-agent row with cost, tokens, and steps. Scale SWE-bench Pro only counts when the public shared-harness board has a row we can fetch. A higher lab number is usually a different test, not a better one.
No Continuum hosted id is verified for this model, so this page does not invent a curl target. Use the first-party API or the OpenRouter slug nvidia/nemotron-nano-9b-v2:free.