DeepSeek-R1 vs Reasoning Model Competitors - o1, o3, Claude, Gemini
When DeepSeek-R1 dropped in January 2025, it shook the AI world by matching OpenAI o1 on reasoning benchmarks using pure reinforcement learning – and releasing the weights. Over a year later, the reasoning model landscape has shifted dramatically. Let’s see where things stand in March 2026.
The Contenders
DeepSeek-R1 (+ R1-0528) – The open-weight 671B MoE model that proved you don’t need proprietary data pipelines to build frontier reasoning. The May 2025 update (R1-0528) pushed AIME 2025 from 70.0% to 87.5% and GPQA Diamond from 71.5% to 81.0%.
OpenAI o3 & o4-mini – OpenAI’s latest reasoning lineup. o3 is the heavyweight with a Codeforces Elo of 2727 (better than most competitive programmers). o4-mini is the cost-efficient variant that punches well above its weight on math benchmarks.
Claude Opus 4.6 & Sonnet 4.6 – Anthropic’s February 2026 releases. Opus 4.6 leads on GPQA Diamond at 91.3% and scores 80.7% on SWE-bench Verified. Sonnet 4.6 delivers near-flagship reasoning at mid-tier pricing.
Gemini 2.5 Pro – Google’s thinking model with a massive 1M token context window. Strong across the board on math and science, and competitive pricing with a generous free tier.
Benchmark Comparison
| Benchmark | DeepSeek-R1-0528 | OpenAI o3 | OpenAI o4-mini | Claude Opus 4.6 | Gemini 2.5 Pro |
|---|---|---|---|---|---|
| AIME 2025 | 87.5% | 88.9% | 93.4% | – | 86.7% |
| MATH-500 | 97.3% | 97.8% | 97.6% | – | – |
| GPQA Diamond | 81.0% | 87.7% | 81.4% | 91.3% | 84.0% |
| Codeforces Elo | ~2029 | 2727 | – | – | – |
| SWE-bench Verified | – | – | – | 80.7% | – |
| MMLU-Pro | 84.0% | – | – | – | – |
A few things jump out. OpenAI o3 dominates on competitive programming with that 2727 Codeforces Elo. Claude Opus 4.6 leads the pack on PhD-level science reasoning (GPQA Diamond 91.3%). DeepSeek-R1-0528 hangs with the best on math despite being fully open-weight. o4-mini is quietly the AIME champion at 93.4%.
Note: not all models have been evaluated on all benchmarks, and some scores use different evaluation protocols (pass@1 vs consensus). Take cross-model comparisons with appropriate nuance.
Cost and Availability
| Model | Input (per 1M tokens) | Output (per 1M tokens) | Open Weights |
|---|---|---|---|
| DeepSeek-R1 | $0.55 | $2.19 | Yes (671B MoE) |
| OpenAI o3 | $2.00 | $8.00 | No |
| OpenAI o4-mini | $1.10 | $4.40 | No |
| Claude Opus 4.6 | $5.00 | $25.00 | No |
| Claude Sonnet 4.6 | $3.00 | $15.00 | No |
| Gemini 2.5 Pro | $1.25 | $10.00 | No |
DeepSeek-R1 is the clear cost leader – roughly 4x cheaper than o3 on input and output. For high-volume reasoning workloads, that gap adds up fast. DeepSeek also offers off-peak discounts up to 75%.
Gemini 2.5 Pro sits in a good middle ground: cheap input tokens and a free tier via Google AI Studio. Claude Opus 4.6 is the most expensive but offers batch API at 50% off and prompt caching at 0.1x for reads.
The Open-Source Advantage
This is where DeepSeek-R1 stands alone among frontier reasoning models. You get:
- Full model weights on Hugging Face (671B parameters, MoE architecture)
- Distilled variants – Qwen-32B, Llama-70B, and smaller models down to 1.5B parameters that retain strong reasoning
- Self-hosting freedom – run on your own infrastructure with no per-token costs, full data privacy, and no rate limits
- Community ecosystem – the Open-R1 project by Hugging Face is replicating the training pipeline
The distilled models are particularly compelling. DeepSeek-R1-Distill-Qwen-32B scores 72.6% on AIME 2024 – better than many proprietary models from a year ago – and runs on a single high-end GPU.
None of the other models here offer open weights. OpenAI, Anthropic, and Google all keep their reasoning models fully proprietary. If you need on-premise deployment, regulatory compliance, or just want to avoid vendor lock-in, DeepSeek is your only option at this performance tier.
When to Pick Each
Pick DeepSeek-R1 when:
- Cost is a primary concern
- You need self-hosted / on-premise deployment
- You want to fine-tune or distill for a specific domain
- You need strong math and coding reasoning on a budget
Pick OpenAI o3 when:
- You need the best competitive programming and code generation
- You’re already in the OpenAI ecosystem
- Peak reasoning performance matters more than cost
Pick OpenAI o4-mini when:
- You want strong reasoning at moderate cost
- Math-heavy workloads (it’s the AIME leader)
- You need a good balance of speed and accuracy
Pick Claude Opus 4.6 when:
- PhD-level science reasoning is critical (GPQA Diamond leader)
- Software engineering tasks (SWE-bench leader)
- You need the 1M token context window at standard pricing
- Agentic coding workflows
Pick Claude Sonnet 4.6 when:
- You want near-Opus reasoning at ~60% of the cost
- High-volume production workloads where Opus is too expensive
Pick Gemini 2.5 Pro when:
- You need long-context reasoning (1M tokens)
- Multimodal reasoning over documents, images, video
- You want competitive performance with Google Cloud integration
- Budget-conscious with the free tier
Final Thoughts
The reasoning model space has fragmented in a healthy way. There’s no single “best” model – it depends on your workload, budget, and deployment requirements.
DeepSeek-R1’s position is unique: it’s the only frontier reasoning model you can actually download, inspect, modify, and self-host. That alone makes it indispensable for the open-source AI ecosystem, even if proprietary models edge it out on specific benchmarks.
For most developers, the practical choice often comes down to: DeepSeek-R1 for cost-sensitive and self-hosted use cases, o3/o4-mini for peak math and code performance, Claude for science reasoning and software engineering, and Gemini for long-context multimodal work.
The competition is fierce and prices keep dropping. That’s good for everyone building with AI.
Try these models side-by-side on Netwrck to see which fits your use case.
