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: 163,840 tokens.
Max completion tokens on that row: 32,768.
Input / output on that row: $0.50 / $2.15 per 1M tokens.
Architecture fields: text->text · DeepSeek · deepseek-r1.
Hugging Face id on that row: deepseek-ai/DeepSeek-R1-0528.
May 28th update to the [original DeepSeek R1](/deepseek/deepseek-r1) Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens.
When you want the 2025-05-28 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 | 40.3% | Printed on the AA Intelligence Evaluations grid for DeepSeek R1 0528 (May '25). Not a DeepSWE chip. | Artificial Analysis DeepSeek R1 0528 | source | 2026-08-19 |
| Humanity's Last Exam | 15.8% | Printed on the AA Intelligence Evaluations grid for DeepSeek R1 0528 (May '25). Not a DeepSWE chip. | Artificial Analysis DeepSeek R1 0528 | source | 2026-08-19 |
| GPQA Diamond | 81.3% | Printed on the AA Intelligence Evaluations grid for DeepSeek R1 0528 (May '25). Not a DeepSWE chip. | Artificial Analysis DeepSeek R1 0528 | source | 2026-08-19 |
| CritPt | 1.4% | Printed on the AA Intelligence Evaluations grid for DeepSeek R1 0528 (May '25). Not a DeepSWE chip. | Artificial Analysis DeepSeek R1 0528 | source | 2026-08-19 |
| AA-Omniscience Accuracy | 30.5% | Printed on the AA Intelligence Evaluations grid for DeepSeek R1 0528 (May '25). Not a DeepSWE chip. | Artificial Analysis DeepSeek R1 0528 | source | 2026-08-19 |
| AA-LCR | 56.7% | Printed on the AA Intelligence Evaluations grid for DeepSeek R1 0528 (May '25). Not a DeepSWE chip. | Artificial Analysis DeepSeek R1 0528 | 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: deepseek-ai/DeepSeek-R1-0528. 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-000163.safetensors | 5,234,139,343 |
model-00002-of-000163.safetensors | 4,302,383,966 |
model-00003-of-000163.safetensors | 4,302,384,375 |
model-00004-of-000163.safetensors | 4,302,349,996 |
model-00005-of-000163.safetensors | 4,302,384,154 |
model-00006-of-000163.safetensors | 4,372,073,602 |
model-00007-of-000163.safetensors | 4,306,080,097 |
model-00008-of-000163.safetensors | 4,302,384,356 |
model-00009-of-000163.safetensors | 4,302,350,190 |
model-00010-of-000163.safetensors | 4,302,383,960 |
model-00011-of-000163.safetensors | 4,302,384,375 |
model-00012-of-000163.safetensors | 1,321,583,941 |
model-00013-of-000163.safetensors | 4,302,317,244 |
model-00014-of-000163.safetensors | 4,302,384,328 |
model-00015-of-000163.safetensors | 4,302,350,218 |
model-00016-of-000163.safetensors | 4,302,383,932 |
model-00017-of-000163.safetensors | 4,302,384,377 |
model-00018-of-000163.safetensors | 4,302,350,026 |
model-00019-of-000163.safetensors | 4,302,384,124 |
model-00020-of-000163.safetensors | 4,302,384,377 |
model-00021-of-000163.safetensors | 4,302,350,413 |
model-00022-of-000163.safetensors | 4,302,384,900 |
model-00023-of-000163.safetensors | 4,302,350,808 |
model-00024-of-000163.safetensors | 4,302,384,504 |
model-00025-of-000163.safetensors | 4,302,384,961 |
model-00026-of-000163.safetensors | 4,302,350,620 |
model-00027-of-000163.safetensors | 4,302,384,692 |
model-00028-of-000163.safetensors | 4,302,384,963 |
model-00029-of-000163.safetensors | 4,302,350,448 |
model-00030-of-000163.safetensors | 4,302,384,884 |
model-00031-of-000163.safetensors | 4,302,350,824 |
model-00032-of-000163.safetensors | 4,302,384,488 |
model-00033-of-000163.safetensors | 4,302,384,963 |
model-00034-of-000163.safetensors | 1,747,417,474 |
model-00035-of-000163.safetensors | 4,302,317,817 |
model-00036-of-000163.safetensors | 4,302,384,914 |
model-00037-of-000163.safetensors | 4,302,350,794 |
model-00038-of-000163.safetensors | 4,302,384,518 |
model-00039-of-000163.safetensors | 4,302,384,963 |
model-00040-of-000163.safetensors | 4,302,350,602 |
model-00041-of-000163.safetensors | 4,302,384,710 |
model-00042-of-000163.safetensors | 4,302,384,963 |
model-00043-of-000163.safetensors | 4,302,350,432 |
model-00044-of-000163.safetensors | 4,302,384,900 |
model-00045-of-000163.safetensors | 4,302,350,808 |
model-00046-of-000163.safetensors | 4,302,384,504 |
model-00047-of-000163.safetensors | 4,302,384,961 |
model-00048-of-000163.safetensors | 4,302,350,620 |
model-00049-of-000163.safetensors | 4,302,384,692 |
model-00050-of-000163.safetensors | 4,302,384,963 |
model-00051-of-000163.safetensors | 4,302,350,448 |
model-00052-of-000163.safetensors | 4,302,384,884 |
model-00053-of-000163.safetensors | 4,302,350,824 |
model-00054-of-000163.safetensors | 4,302,384,488 |
model-00055-of-000163.safetensors | 4,302,384,963 |
model-00056-of-000163.safetensors | 1,747,417,474 |
model-00057-of-000163.safetensors | 4,302,317,817 |
model-00058-of-000163.safetensors | 4,302,384,914 |
model-00059-of-000163.safetensors | 4,302,350,794 |
model-00060-of-000163.safetensors | 4,302,384,518 |
model-00061-of-000163.safetensors | 4,302,384,963 |
model-00062-of-000163.safetensors | 4,302,350,602 |
model-00063-of-000163.safetensors | 4,302,384,710 |
model-00064-of-000163.safetensors | 4,302,384,963 |
model.safetensors.index.json | 8,898,324 |
tokenizer.json | 7,847,602 |
figures/benchmark.png | 322,500 |
modeling_deepseek.py | 75,741 |
README.md | 14,767 |
configuration_deepseek.py | 9,897 |
tokenizer_config.json | 3,957 |
config.json | 1,660 |
.gitattributes | 1,561 |
LICENSE | 1,084 |
generation_config.json | 171 |
Showing the first 64 of 163 safetensor shards, plus 11 other files. The snapshot was capped at 64 shards when the tree API was read on 2026-08-19, so the remaining 99 are on the Files tab, not on this page. Totals above are the full tree API sum, not this slice. Byte sizes are Hub tree API size fields, never invented.
OpenRouter slug deepseek/deepseek-r1-0528 on the 2026-08-19 catalog.
First party: https://api-docs.deepseek.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: $0.50 in / $2.15 out per 1M tokens. Context 163,840 in / 32,768 out.
No separate internal-reasoning price on that row.
| Claim | Source | As of |
|---|---|---|
| OpenRouter id deepseek/deepseek-r1-0528; context 163840; created 1748455170; $0.50 / $2.15 per 1M | OpenRouter /api/v1/models | 2026-08-19 |
| AA Intelligence Index 20; 6 printed Index benches | Artificial Analysis model page | 2026-08-19 |
| OpenRouter id deepseek/deepseek-r1-0528, context 163,840 | OpenRouter /api/v1/models | 2026-08-19 |
| HF downloads 132,584 | Hugging Face API deepseek-ai/DeepSeek-R1-0528 | 2026-08-19 |
| HF safetensors.total: 684,531,386,000 stored tensors; HF API usedStorage 688,588,119,234 bytes; created 2025-05-28T09:46:42.000Z; HF tensors BF16 + F8_E4M3 + F32 | Hugging Face API deepseek-ai/DeepSeek-R1-0528 | 2026-08-19 |
| 163 safetensor shards; shard bytes 688,586,727,753; tree files 174 | Hugging Face tree API deepseek-ai/DeepSeek-R1-0528 | 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 DeepSeek's model family with DeepSeek V3 0324.
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 deepseek/deepseek-r1-0528.