DeepSeek R1
deepseek-r1
70% in · 30% out mix
Higher = better value
Speed
60/100
Context
128K
Tier
power
DeepSeek V3.2
deepseek-v3-2
70% in · 30% out mix
Higher = better value
Speed
88/100
Context
128K
Tier
fast
IN-DEPTH ANALYSIS
DeepSeek R1 vs DeepSeek V3.2: Detailed Comparison
DeepSeek R1 is DeepSeek's flagship-tier language model with a 128K-token context window, excelling at reasoning. DeepSeek V3.2 from DeepSeek is a lightweight-tier model supporting 128K tokens in context, with standout performance in coding.
DeepSeek R1 costs 3.5x what DeepSeek V3.2 does per blended million tokens. That is a steep premium, and it buys a 21-point lead on combined coding and reasoning. Whether that trade is worth it depends entirely on how much of your traffic actually needs the harder model — for most workloads the honest answer is a small fraction of it, which is an argument for routing rather than for picking one. DeepSeek R1 is priced at $0.55/M input tokens and $2.19/M output tokens. DeepSeek V3.2 costs $0.26/M input and $0.38/M output.
In independent benchmark evaluations, DeepSeek R1 leads with coding scores of 92/100 and reasoning scores of 97/100, compared to DeepSeek V3.2's 85/100 in coding and 83/100 in reasoning.
Capability breakdown
Across the five core benchmark categories, here is how DeepSeek R1 and DeepSeek V3.2 stack up head to head:
Best model by task
- coding: DeepSeek R1 wins with 92/100
- reasoning: DeepSeek R1 wins with 97/100
- data extraction: DeepSeek R1 wins with 84/100
- creative tasks: DeepSeek R1 wins with 82/100
Estimated monthly cost at scale
At 10M + 2M per month, DeepSeek R1 runs about $9.88 while DeepSeek V3.2 runs about $3.36 — DeepSeek V3.2 saves roughly $6.52 (66%) every month.
What actually decides it
Both models come from DeepSeek, so they share a tokenizer, an API surface, and a billing account. Switching between them is a model-string change and nothing else — no new SDK, no re-tokenizing your prompts to re-estimate cost, no second vendor to onboard. That makes routing between them far cheaper to implement than a cross-vendor split.
DeepSeek V3.2 and DeepSeek R1 are 15 months apart, which is more than one generation in this market. Benchmark comparisons across that gap flatter the older model: it was measured against the evaluations that existed at the time. Treat DeepSeek R1's scores as a floor for what it does well and be sceptical of a close-looking result.
Context is effectively a tie: 128K against 128K. Neither model unlocks a document size the other cannot handle, so this axis should not enter the decision. If you were hoping context would break the tie for you, it will not.
Latency separates them: DeepSeek V3.2 scores 88/100 against 60/100 for DeepSeek R1, a 28-point gap. That matters for anything a person waits on — chat, autocomplete, interactive tools. For batch and background work it does not, and trading latency for capability there is usually the right call.
DeepSeek R1 is 3.5x the price of DeepSeek V3.2. Reserve it for the requests that actually need it and route the rest to DeepSeek V3.2 — that hybrid beats either model used alone on cost per useful answer.
Benchmark Comparison
Head-to-head scores across 5 categories — sourced from official evals
Coding
Reasoning
Extraction
Creative
Vision
Speed Score
Context Window
What Is a Token?
Models don't read words — they process tokens.
A token is roughly 4 characters of English text (~¾ of a word). Your API bill is priced per million tokens — understanding this directly reduces your costs.
Short phrase
"Hello, world!"
- DeepSeek R1
- $0.22
- DeepSeek V3.2
- $0.10
Business email
One typical email (~200 words)
- DeepSeek R1
- $14.85
- DeepSeek V3.2
- $7.02
Code file
50-line Python script
- DeepSeek R1
- $22.00
- DeepSeek V3.2
- $10.40
Prices shown are for 100,000 runs of each workload — one run costs a fraction of a cent on both DeepSeek R1 and DeepSeek V3.2, so the number only becomes meaningful at production volume. Input tokens only; add your output volume in the calculator below.
How to check your token usage
response.usage.total_tokensEvery API response includes a usage object. Sum total_tokens across all calls to get your monthly figure, then use the calculator below.
Your Cost Calculator
Enter your actual monthly token usage to see real savings
Quick Presets
DeepSeek R1
$31.26/mo
$375.12/yr
DeepSeek V3.2
$8.88/mo
$106.56/yr
Annual Savings
$268.56 saved per year
DeepSeek V3.2 cheaper · $22.38/mo
Deep-Dive Audit — DeepSeek R1 & DeepSeek V3.2
Surgically Auditing: Deep Logic
3-YEAR STRATEGIC LOSS PROJECTION
$10.044
Without optimization protocols, current model choices will result in $3.348 capital loss per year.
EFFICIENCY SCORE
97%
This model achieves a 97 benchmark score in this category.
CATEGORY GAP
3 pts
Distance from Leader
Competitive Landscape Analysis
Source: MMLU-Pro + GPQA Diamond (Apr 2026)
Category Champion: Claude Fable 5
According to MMLU-Pro + GPQA Diamond (Apr 2026) data, Claude Fable 5 provides the optimum balance for Deep Logic tasks.
Market Score
%100
Savings Rate
%10
Operational Prescription
- Implement model cascading to optimize token spend.
- Analyze complex_reasoning data to leverage local semantic caching.
COST AUDIT PROTOCOL
Categorical Fit
"DeepSeek R1 scores 97 in this category — a well-matched choice."
Categorical Alternative Opportunity
"Claude Fable 5 leads this category with 100 points according to MMLU-Pro + GPQA Diamond (Apr 2026) data."
Inertia Tax Detected
"85% of traffic can be routed to cheaper models. Fast tier (GPT-5 Nano) and Smart tier (o3-mini) can save $0.28/month."
3-Tier Intelligent Routing Architecture
10% SAVINGS VIA ROUTINGGPT-5 Nano
IQ Score: 72/100
$18.00/yr
o3-mini
IQ Score: 97/100
$277.20/yr
DeepSeek R1
IQ Score: 97/100
$59.184/yr
Without tiered routing, you pay the 'Inertia Tax' — routing all traffic to the most expensive model regardless of task complexity. Tiered cascade eliminates $40.176/year in avoidable overhead.
Deep Logic — Model Cost / Quality Matrix
Source: MMLU-Pro + GPQA Diamond (Apr 2026)| Model | Benchmark | Input (per M) | Output (per M) | Annual Cost* | Value Index |
|---|---|---|---|---|---|
Claude Fable 5LEADER | 100/100 | $10.00 | $50.00 | $720.00 | 1/100 |
GPT-5.6 Sol | 99/100 | $5.00 | $30.00 | $420.00 | 1/100 |
Claude Opus 5 | 98/100 | $5.00 | $25.00 | $360.00 | 1/100 |
Claude Opus 4.6 | 98/100 | $5.00 | $25.00 | $360.00 | 1/100 |
DeepSeek R1SELECTED | 97/100 | $0.55 | $2.19 | $32.88 | 12/100 |
Claude Opus 4.8 | 96/100 | $5.00 | $25.00 | $360.00 | 1/100 |
DeepSeek V3 | 91/100 | $0.28 | $0.42 | $8.40 | 45/100 |
Claude 3 Opus | 90/100 | $15.00 | $75.00 | $1,080.00 | 0/100 |
Llama 3.1 405B | 88/100 | $2.70 | $2.70 | $64.80 | 6/100 |
GPT-5.6 Luna | 85/100 | $0.20 | $1.20 | $16.80 | 21/100 |
DeepSeek V3.2 | 83/100 | $0.26 | $0.38 | $7.68 | 45/100 |
Claude Haiku 4.5 | 82/100 | $1.00 | $5.00 | $72.00 | 5/100 |
Gemini 2.0 Flash | 81/100 | $0.10 | $0.40 | $6.00 | 56/100 |
Claude 3.5 Haiku | 80/100 | $0.80 | $4.00 | $57.60 | 6/100 |
Llama 3 70B | 79/100 | $0.65 | $2.75 | $40.80 | 8/100 |
GPT-4o Mini | 78/100 | $0.15 | $0.60 | $9.00 | 36/100 |
Gemini 1.5 Flash | 76/100 | $0.07 | $0.30 | $4.50 | 70/100 |
GPT-5 NanoBEST VALUE | 72/100 | $0.10 | $0.15 | $3.00 | 100/100 |
Claude 3 Haiku | 70/100 | $0.25 | $1.25 | $18.00 | 16/100 |
* Annual cost for given volumes. Value Index = Score / Cost (Higher = Best Value).
// iOPTERA Surgical Routing Wrapper
const auditModel = async (prompt: string) => {
const complexity = measureComplexity(prompt);
// Tactical Cascade Logic
if (complexity < 0.45) {
// Redirect simple tasks to efficient model
return await llm.call("iOPTERA Optimization", prompt);
}
// High-latency routing for complex reasoning
return await llm.call("Claude Fable 5", prompt);
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