Agentic
Not comparable- DeepSeek-R1
- Not ranked
- GPT Realtime 2.1 Mini
- Not ranked
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Not comparable
DeepSeek-R1 vs GPT Realtime 2.1 Mini
Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.
0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
Which one for your work
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Chat turn cost
1K fresh input + 500 output tokens
DeepSeek-R1
DeepSeek-R1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
DeepSeek-R1
DeepSeek-R1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
DeepSeek-R1 and GPT Realtime 2.1 Mini are not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
DeepSeek-R1 and GPT Realtime 2.1 Mini are not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. DeepSeek-R1 does not fit this workload in one request. GPT Realtime 2.1 Mini does not fit this workload in one request.
Confidence: listed-rates
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
Evidence parity totals are not available.
- Shared results
- 0
- DeepSeek-R1 only
- 0
- GPT Realtime 2.1 Mini only
- 0
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
Coding
Not comparable- DeepSeek-R1
- Not ranked
- GPT Realtime 2.1 Mini
- Not ranked
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- DeepSeek-R1
- 59.3
- Unranked · 2 rankable rows
- GPT Realtime 2.1 Mini
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- DeepSeek-R1
- 47.6
- Estimated · #96/183
- GPT Realtime 2.1 Mini
- Not ranked
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Not comparable
Math
Not comparable- DeepSeek-R1
- Not ranked
- GPT Realtime 2.1 Mini
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- DeepSeek-R1
- Not ranked
- GPT Realtime 2.1 Mini
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- DeepSeek-R1
- Not ranked
- GPT Realtime 2.1 Mini
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- DeepSeek-R1
- 46.1
- #90/123
- GPT Realtime 2.1 Mini
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | DeepSeek-R1 | GPT Realtime 2.1 Mini | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign lane · 0 vs 0 public rows | Not comparable |
| Coding | Not ranked | Not ranked | Not comparableBenchAlign lane · 0 vs 0 public rows | Not comparable |
| Reasoning | 59.3Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 47.6Estimated · #96/183 | Not ranked | Not comparableBenchAlign lane · 0 vs 0 public rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 46.1#90/123 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
What each workload costs
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
Chat turn
1K fresh input + 500 output tokens
- DeepSeek-R1
- $0.00165
- Fits in one request
- GPT Realtime 2.1 Mini
- $0.0018
- Fits in one request
DeepSeek-R1 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- DeepSeek-R1
- $0.03407
- Fits in one request
- GPT Realtime 2.1 Mini
- $0.0372
- Fits in one request
DeepSeek-R1 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- DeepSeek-R1
- $0.0609
- Does not fit in one request
- GPT Realtime 2.1 Mini
- $0.048
- Does not fit in one request
DeepSeek-R1 does not fit this workload in one request. GPT Realtime 2.1 Mini does not fit this workload in one request.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationDeepSeek-R1GPT Realtime 2.1 Mini
Context window
Maximum documented context; output-token limits may be lower.
DeepSeek-R1
128K
GPT Realtime 2.1 Mini
API model ID
DeepSeek-R1
Not sourced
GPT Realtime 2.1 Mini
gpt-realtime-2.1-mini
OpenAI GPT Realtime 2.1 Mini model documentationCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
DeepSeek-R1
$0.14 per 1M cached input tokens
GPT Realtime 2.1 Mini
$0.06 per 1M cached input tokens
OpenAI model documentationDocumented inputs
DeepSeek-R1
Not sourced
GPT Realtime 2.1 Mini
text, audio, image
OpenAI GPT Realtime 2.1 Mini model documentationDocumented outputs
DeepSeek-R1
Not sourced
GPT Realtime 2.1 Mini
Provider availability
DeepSeek-R1
Not sourced
GPT Realtime 2.1 Mini
Generally Available · OpenAI API
OpenAI GPT Realtime 2.1 Mini model documentationReasoning profile
DeepSeek-R1
Reasoning
GPT Realtime 2.1 Mini
Non-Reasoning
Weight access
DeepSeek-R1
Open Weight
GPT Realtime 2.1 Mini
Proprietary
License
DeepSeek-R1
Open Weight
GPT Realtime 2.1 Mini
Proprietary
Release date
DeepSeek-R1
2025-01-20
GPT Realtime 2.1 Mini
2026-07-06
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- Repository review: $0.03407 vs $0.0372. Cache-heavy agent loop: $0.0609 vs $0.048.
- Context tradeoff
- Both models list 128K.
Run the same representative tasks against both endpoints before changing production traffic.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
DeepSeek-R1
API / mo$2,055
Self-host / mo$18,221
Break-even583M/day
GPT Realtime 2.1 Mini
API / mo$2,250
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Frequently asked questions
Which is better, DeepSeek-R1 or GPT Realtime 2.1 Mini?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, DeepSeek-R1 or GPT Realtime 2.1 Mini?
DeepSeek-R1 and GPT Realtime 2.1 Mini are not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, DeepSeek-R1 or GPT Realtime 2.1 Mini?
DeepSeek-R1 and GPT Realtime 2.1 Mini are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, DeepSeek-R1 or GPT Realtime 2.1 Mini?
For the stated presets, chat costs $0.00164 on DeepSeek-R1 and $0.0018 on GPT Realtime 2.1 Mini; repository review costs $0.03407 and $0.0372; the cache-heavy agent loop costs $0.0609 and $0.048. DeepSeek-R1 does not fit this workload in one request. GPT Realtime 2.1 Mini does not fit this workload in one request.
Which has the larger context window, DeepSeek-R1 or GPT Realtime 2.1 Mini?
Both models list the same context window, 128K.
Related comparisons
Last updated September 10, 2026
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