Model comparison
DeepSeek-R1 vs GPT-5 (medium)
Head-to-head evidence from 11 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek-R1 #103 (Supported); GPT-5 (medium) #84 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek-R1 and GPT-5 (medium) share 11 comparable benchmark results. 0 of 8 categories are comparable. 0 results are unique to DeepSeek-R1; 3 to GPT-5 (medium).
Updated July 20, 2026- Shared results
- 11
- DeepSeek-R1 only
- 0
- GPT-5 (medium) only
- 3
- Comparable categories
- 0 / 8
Benchmark data for DeepSeek-R1 and GPT-5 (medium) is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 11 shared benchmark results across 5 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek-R1 | GPT-5 (medium) | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek-R1$0.55 input / $2.19 output | GPT-5 (medium)Not available | A complete price comparison is not available. |
| Generation speedtokens per second | DeepSeek-R1Not available | GPT-5 (medium)83 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek-R1Not available | GPT-5 (medium)36.28 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek-R1128K | GPT-5 (medium)128K | Listed context windows are equal. |
Benchmark Deep Dive
Agentic1 benchmarks
| Benchmark | DeepSeek-R1 | GPT-5 (medium) | Result |
|---|---|---|---|
| τ²-bench resultsSource | 36.5% | 86.5% | GPT-5 (medium) leads |
Coding1 benchmarks
| Benchmark | DeepSeek-R1 | GPT-5 (medium) | Result |
|---|---|---|---|
| AA-SciCodeSource | 40.3% | 41.1% | GPT-5 (medium) leads |
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | DeepSeek-R1 | GPT-5 (medium) | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 20.1% | 33.7% | GPT-5 (medium) leads |
| AA-GPQA DiamondSource | 81.3% | 84.2% | GPT-5 (medium) leads |
| AA-HLESource | 14.9% | 23.5% | GPT-5 (medium) leads |
| AA-Omniscience IndexSource | -27.1% | -10.1% | GPT-5 (medium) leads |
| AA-Omniscience AccuracySource | 31.0% | 38.9% | GPT-5 (medium) leads |
| AA-Omniscience Hallucination RateSource | 84.0% | 80.1% | GPT-5 (medium) leads |
Math1 benchmarks
| Benchmark | DeepSeek-R1 | GPT-5 (medium) | Result |
|---|---|---|---|
| AA MATH-500Source | — | 99.1% | Not comparable |
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | DeepSeek-R1 | GPT-5 (medium) | Result |
|---|---|---|---|
| AA-IFBenchSource | 39.6% | 70.6% | GPT-5 (medium) leads |
Frequently Asked Questions (3)
Can I compare DeepSeek-R1 and GPT-5 (medium) on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for DeepSeek-R1 and GPT-5 (medium) today?
DeepSeek-R1: $0.55 input / $2.19 output per 1M tokens GPT-5 (medium): Pricing unavailable Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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