SAVRN Model Hub · Comparisons
DeepSeek-V4-Flash-DSpark vs gpt-oss-120b
DeepSeek-V4-Flash-DSpark has 165.3B parameters and gpt-oss-120b has 116.8B parameters; DeepSeek-V4-Flash-DSpark is released under MIT License and gpt-oss-120b under Apache License 2.0; at 16-bit, DeepSeek-V4-Flash-DSpark needs about 396.6 GB (2x MI325X from $4.00 an hour) and gpt-oss-120b about 280.4 GB (1x MI355X from $2.59 an hour).
| Field | DeepSeek-V4-Flash-DSpark deepseek-ai/DeepSeek-V4-Flash-DSpark | gpt-oss-120b openai/gpt-oss-120b |
|---|---|---|
| Publisher | DeepSeek | OpenAI |
| Task | Text generation | Text generation |
| Modality | Text | Text |
| Parameters, as reported | 165.3B parameters | 116.8B parameters |
| Architecture | DeepseekV4ForCausalLM | GptOssForCausalLM |
| Library | transformers | transformers |
| Context length | 1,048,576 tokens | 131,072 tokens |
| Repository size | 166.9 GB | 195.8 GB |
| Artifact formats | safetensors | safetensors |
| License | mit | apache-2.0 |
| Access | Open weights, no gate | Open weights, no gate |
| Memory at 16-bit (weights and margin) | 396.6 GB | 280.4 GB |
| Cheapest GPUs at 16-bit, per hour | 2x MI325X, $4.00 | 1x MI355X, $2.59 |
| Memory at 4-bit (weights and margin) | 99.2 GB | 70.1 GB |
| Cheapest GPUs at 4-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Revision viewed | 62af8fffb2f7 | b5c939de8f75 |
| Downloads reported by the hub | 1M | 5.2M |
| Last observed | 2026-09-18 | 2026-09-18 |
An evaluation row appears only where at least two of these models report the same benchmark with the same stated configuration, metric, unit and setup. Different evaluators stay named in each cell. Values are shown as reported: no unit conversion, no ranking.
Other Reported Results
These results are listed for each model on its own, because the conditions needed to compare them are not stated or do not match. Two results that leave a condition blank are not assumed to share it.
gpt-oss-120b
| Benchmark | Conditions | Result | Reported by | Revision | Date |
|---|---|---|---|---|---|
| Idavidrein/gpqa | Task diamondMetric diamondSetup GPQA DiamondComparison conditions not established | 80.8081 | EvalEval Reported by a third party |
Evaluated revision not stated | 2026-04-16 |
| Idavidrein/gpqa | Task diamondMetric diamondSetup Reasoning: mediumComparison conditions not established | 73.1 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| Idavidrein/gpqa | Task diamondMetric diamondSetup Reasoning: high, With toolsComparison conditions not established | 80.9 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| Idavidrein/gpqa | Task diamondMetric diamondSetup Reasoning: highComparison conditions not established | 80.1 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| Idavidrein/gpqa | Task diamondMetric diamondSetup Reasoning: low, With toolsComparison conditions not established | 68.1 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| Idavidrein/gpqa | Task diamondMetric diamondSetup Reasoning: medium, With toolsComparison conditions not established | 73.5 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| Idavidrein/gpqa | Task diamondMetric diamondSetup Reasoning: lowComparison conditions not established | 67.1 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| LEXam-Benchmark/LEXam | Task mcq_4_choicesMetric mcq_4_choicesComparison conditions not established | 47.71 | LEXam Leaderboard Reported by a third party |
Evaluated revision not stated | 2026-06-02 |
| LEXam-Benchmark/LEXam | Task open_questionMetric open_questionComparison conditions not established | 51.74 | LEXam Leaderboard Reported by a third party |
Evaluated revision not stated | 2026-06-02 |
| SWE-bench/SWE-bench_Verified | Task swe_bench_%_resolvedMetric swe_bench_%_resolvedSetup Reasoning: lowComparison conditions not established | 47.9 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| SWE-bench/SWE-bench_Verified | Task swe_bench_%_resolvedMetric swe_bench_%_resolvedSetup Reasoning: mediumComparison conditions not established | 52.6 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| SWE-bench/SWE-bench_Verified | Task swe_bench_%_resolvedMetric swe_bench_%_resolvedSetup Reasoning: highComparison conditions not established | 62.4 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| ScaleAI/SWE-bench_Pro | Task SWE_Bench_ProMetric SWE_Bench_ProComparison conditions not established | 16.2 | SWE-Bench Pro official evaluation results Reported by a third party |
Evaluated revision not stated | 2026-02-28 |
| TIGER-Lab/MMLU-Pro | Task mmlu_proMetric mmlu_proComparison conditions not established | 80.8 | EvalEval Reported by a third party |
Evaluated revision not stated | 2026-06-30 |
| cais/hle | Task hleMetric hleSetup Reasoning: lowComparison conditions not established | 5.2 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| cais/hle | Task hleMetric hleSetup Reasoning: medium, With toolsComparison conditions not established | 11.3 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| cais/hle | Task hleMetric hleSetup Reasoning: mediumComparison conditions not established | 8.6 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| cais/hle | Task hleMetric hleSetup Reasoning: highComparison conditions not established | 14.9 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| cais/hle | Task hleMetric hleSetup Reasoning: high, With toolsComparison conditions not established | 19 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| cais/hle | Task hleMetric hleSetup Reasoning: low, With toolsComparison conditions not established | 9.1 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| joelniklaus/LEXam-hard | Task lexam_hardMetric lexam_hardSetup lighteval, LEXam paper prompts, one response per question, no tools; DeepSeek-R1-0528 judge; mean of the German and English means over the 518 questions, 0-100Comparison conditions not established | 37.55 | SwissLegalEvals per-sample details (lighteval) Reported by a third party |
Evaluated revision not stated | 2026-06-11 |
SAVRN's Notes on DeepSeek-V4-Flash-DSpark
The publisher says it outright: DSpark is not a new model. It is the DeepSeek-V4-Flash checkpoint with a speculative decoding module attached, so you are evaluating a serving path, not fresh weights. Those weights run 165.3 billion parameters across 256 routed experts with 6 active per token. Precision picks the hardware: 16-bit needs 396.6 GB and two MI325X cards at $4.00 per hour, 8-bit needs 198.3 GB on one MI325X at $2.00, and 4-bit needs 99.2 GB on one MI300X at $1.85.
MIT covers it: commercial use, modification and redistribution with the notices kept. Two checks before buying cards. The memory figures are the floor; a request that uses the 1,048,576-token context adds cache on top, sized from the single key/value head at 512 dimensions. And the speculative decoding path is the point of this release, so confirm your serving stack runs the publisher's inference example.
SAVRN's Notes on gpt-oss-120b
Memory decides the hardware. gpt-oss-120b carries 116.8 billion parameters; at 16-bit the working footprint is 280.4 GB, meaning one MI355X with 288 GB at $2.59 an hour. At 8-bit it drops to 140.2 GB and one MI300X with 192 GB at $1.85 an hour covers it, and 4-bit needs 70.1 GB on the same card. For general text generation with a 131,072-token window, we would start on the 8-bit single card.
Apache 2.0 permits commercial use, modification and redistribution, provided the license and copyright notices stay attached and significant changes are stated, plus an express patent grant. Before committing, read the model card at arXiv:2508.10925 and price the hosted route: on the Index, DeepInfra lists $0.037 in and $0.17 out per million tokens, Cerebras $0.35 and $0.75. Once a month of tokens at those rates costs more than an MI300X at $1.85 an hour, run it yourself.
Questions
Which is larger, DeepSeek-V4-Flash-DSpark or gpt-oss-120b?
DeepSeek-V4-Flash-DSpark (165.3B parameters) is larger than gpt-oss-120b (116.8B parameters), by the parameter counts their publishers report.
Which is cheaper to run, DeepSeek-V4-Flash-DSpark or gpt-oss-120b?
At 4-bit, DeepSeek-V4-Flash-DSpark fits on 1x MI300X from $1.85 an hour and gpt-oss-120b on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use DeepSeek-V4-Flash-DSpark commercially?
Yes. DeepSeek-V4-Flash-DSpark is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.
Can I use gpt-oss-120b commercially?
Yes. gpt-oss-120b is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.