DeepSeek R1 and Claude Opus 4.7 sit at opposite ends of the price spectrum but both target high-reasoning, high-complexity tasks. DeepSeek R1 costs $0.55/1M input — 9× cheaper than Opus 4.7's $5/1M — and was built specifically for chain-of-thought reasoning. Claude Opus 4.7 leads on coding (SWE-Bench Pro 64.3%), has a 1M token context window, and is a more capable all-rounder. For pure structured reasoning at budget cost, DeepSeek R1 is exceptional. For frontier coding, complex multi-task workflows, and maximum quality, Claude Opus 4.7 is worth the premium.
DeepSeekBudget
DeepSeek R1
Open-source o1-class reasoning at a fraction of the cost.
VS
AnthropicPremium
Claude Opus 4.7
Previous Opus flagship, now superseded by Claude Opus 4.8 at the same price.
Winner
At a glance
DeepSeek R1
Claude Opus 4.7
Input cost / 1M tokens
$$0.55/1M
$$5.00/1M
Output cost / 1M tokens
$$2.19/1M
$$25.00/1M
Context window
128k tokens
1M tokens
Speed
Deliberate
Deliberate
Price tier
Budget
Premium
Benchmarks
SWE-bench (coding)
49.2%
87.6%
Arena Elo
1,320
1,800
MMLU
90.8%
92%
How they compare
Which model wins for each use case — and why.
ReasoningDeepSeek R1 wins
DeepSeek R1 was purpose-built for chain-of-thought reasoning and matches o1-level performance on math and logic benchmarks at 9× lower cost.
CodingClaude Opus 4.7 wins
Claude Opus 4.7 leads SWE-Bench Pro at 64.3%. DeepSeek R1 is strong at algorithmic problems but Claude is significantly better at real-world software engineering.
PriceDeepSeek R1 wins
DeepSeek R1 at $0.55/1M input is 9× cheaper than Claude Opus 4.7 at $5/1M. For high-volume reasoning tasks, the savings are transformative.
SpeedClaude Opus 4.7 wins
DeepSeek R1 is a deliberate thinking model — slow by design. Claude Opus 4.7 is faster for production workflows where latency matters.
VersatilityClaude Opus 4.7 wins
Claude Opus 4.7 handles coding, writing, vision, and research equally well. DeepSeek R1 is optimised for reasoning and can be inconsistent on diverse open-ended tasks.
Which should you pick?
Pick DeepSeek R1 if…
Your work is primarily mathematical reasoning, logic, or structured problem-solving
You want o1-class reasoning quality at budget API pricing
Cost per token matters enormously — DeepSeek R1 is 9× cheaper on input
Latency is not a constraint and you run reasoning in batch
For most workflows, Claude Opus 4.7 is the stronger choice.
Superseded by Opus 4.8 (May 27, 2026), Opus 5 (July 24) and Opus 5.5 (September 22), which scores 89.9% on SWE-bench Pro against 64.3% here at a lower $4/$20. For existing pinned integrations Opus 4.7 still works, but new deployments should use Claude Opus 5.5.
The case for each model
What each one is genuinely good at, where it falls down, and when we would steer you away from it.
The runner-up here, but not by a wide margin. Against Claude Opus 4.7 it costs about 91% less per token.
Open-source reasoning model that matches o1-class performance on math, science, and complex coding at a fraction of the cost — the best open alternative to proprietary reasoning models.
Input
$0.55/1M
Output
$2.19/1M
Context
128k tokens
Speed
Deliberate
What people actually use it for
Complex algorithm design and mathematical problem-solving where chain-of-thought reasoning matters
Scientific research synthesis requiring structured multi-step analysis
Hard coding challenges and competitive programming at low cost compared to o1
Where it wins
o1-class reasoning performance at under $0.60/1M input tokens
Open-source weights — can be self-hosted for sensitive workloads
Explicit chain-of-thought reasoning makes outputs auditable
Where it falls down
Slow — deliberate reasoning takes significantly longer than standard models
Overkill for routine tasks where a faster model gets the same result
Same data sovereignty concerns as DeepSeek V3 for regulated industries
Skip it if
Speed matters — R1's deliberate reasoning makes it wrong for interactive or high-throughput use cases.
Our verdict
The open-source reasoning model benchmark. If you need o1-class thinking at open-source pricing, nothing else competes.
Full pricing, benchmark table and release notes on the DeepSeek R1 page.
Our overall pick in this comparison. Against DeepSeek R1 it costs about 91% more per token, takes 8x the context and answers faster.
Anthropic's April 2026 Opus flagship, since superseded by Opus 4.8, Opus 5 and Opus 5.5. Still available for pinned API integrations at $5/$25 — more than the stronger Opus 5.5 costs ($4/$20).
Input
$5.00/1M
Output
$25.00/1M
Context
1M tokens
Speed
Deliberate
What people actually use it for
Delegating difficult multi-file engineering work that needs careful verification
Running premium coding agents and autonomous PR review workflows
Reading large codebases, research corpora, or design references with 1M context
Where it wins
64.3% SWE-Bench Pro — still strong, second only to Opus 4.8
1M context window for large codebases and document-heavy workflows
Strong vision accuracy at 98.5% on Vision Bench
Where it falls down
Opus 4.8 launched at the same $5/$25 price with 69.2% SWE-Bench Pro and parallel subagents
No reason to prefer Opus 4.7 for new integrations — use Claude Opus 5.5 instead
Skip it if
You need cheaper high-volume throughput, image generation, or a workflow that must stay inside OpenAI tooling.
Our verdict
Superseded by Opus 4.8 (May 27, 2026), Opus 5 (July 24) and Opus 5.5 (September 22), which scores 89.9% on SWE-bench Pro against 64.3% here at a lower $4/$20. For existing pinned integrations Opus 4.7 still works, but new deployments should use Claude Opus 5.5.
Is DeepSeek R1 better than Claude Opus for reasoning?
For pure mathematical and logical reasoning, DeepSeek R1 matches o1-class performance at 9× lower cost. Claude Opus 4.7 is the stronger all-rounder with better coding and versatility.
How much cheaper is DeepSeek R1 than Claude Opus 4.7?
DeepSeek R1 costs $0.55/1M input and $2.19/1M output. Claude Opus 4.7 costs $5/1M input and $25/1M output — making Claude about 9× more expensive on input and 11× more on output.
Which is better for coding?
Claude Opus 4.7 is significantly better for coding — it leads SWE-Bench Pro at 64.3%. DeepSeek R1 handles algorithmic and math-heavy coding well but isn't built for general software engineering.