Long documents
Prompts that approach the documented context limit
Interfaze Beta
Interfaze Beta has the larger documented context window.
Confidence: documented
DeepSeek V3 vs Interfaze Beta
Updated September 24, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
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Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 1
- DeepSeek V3 only
- 5
- Interfaze Beta only
- 8
- Like-for-like categories
- 0 / 8
Supported: DeepSeek V3How the comparison works
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 V3
DeepSeek V3 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 V3
DeepSeek V3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Interfaze Beta is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Interfaze Beta is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedCache-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 V3 does not fit this workload in one request. Interfaze Beta has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
18.9DeepSeek V3—Interfaze Beta
Not comparable · BenchAlign v5.7
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
5 DeepSeek V3 only
1 Shared
8 Interfaze Beta only
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.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
GPQAKnowledge
Normalized gap 30.8DeepSeek V3:59.1%Interfaze Beta:89.9%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.7 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.
Agentic
Not comparable- DeepSeek V3
- 11.1
- Estimated · #102/105
- Interfaze Beta
- Not ranked
- Basis
- BenchAlign v5.7 lane · 0 vs 0 public rows
- Reading
- Not comparable
Coding
Not comparable- DeepSeek V3
- 18.9
- Estimated · #123/135
- Interfaze Beta
- Not ranked
- Basis
- BenchAlign v5.7 lane · 2 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- DeepSeek V3
- 42.2
- Unranked · 2 rankable rows
- Interfaze Beta
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- DeepSeek V3
- Not ranked
- Interfaze Beta
- 27.6
- Unranked · 5 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- DeepSeek V3
- 30.0
- Estimated · #130/158
- Interfaze Beta
- Not ranked
- Basis
- BenchAlign v5.7 lane · 2 vs 3 public rows
- Reading
- Not comparable
Multilingual
Not comparable- DeepSeek V3
- Not ranked
- Interfaze Beta
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- DeepSeek V3
- 38.2
- #103/124
- Interfaze Beta
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- DeepSeek V3
- 26.0
- Unranked · 1 rankable row
- Interfaze Beta
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
| Category | DeepSeek V3 | Interfaze Beta | Basis | Reading |
|---|---|---|---|---|
| Agentic | 11.1Estimated · #102/105 | Not ranked | Not comparableBenchAlign v5.7 lane · 0 vs 0 public rows | Not comparable |
| Coding | 18.9Estimated · #123/135 | Not ranked | Not comparableBenchAlign v5.7 lane · 2 vs 1 public rows | Not comparable |
| Reasoning | 42.2Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 27.6Unranked · 5 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Knowledge | 30.0Estimated · #130/158 | Not ranked | Not comparableBenchAlign v5.7 lane · 2 vs 3 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 38.2#103/124 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 26.0Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Bars run 0–100Methodology
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 V3
- $0.00082
- Fits in one request
- Interfaze Beta
- $0.00325
- Fits in one request
DeepSeek V3 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- DeepSeek V3
- $0.0168
- Fits in one request
- Interfaze Beta
- $0.0855
- Fits in one request
DeepSeek V3 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 V3
- $0.0304
- Does not fit in one request
- Interfaze Beta
- $0.365
- Fits in one request
- Cached input priced at the published list-input rate
DeepSeek V3 does not fit this workload in one request. Interfaze Beta has no published cached-input rate, so cached tokens use its listed input rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationDeepSeek V3Interfaze Beta
Context window
Maximum documented context; output-token limits may be lower.
DeepSeek V3
128K
Interfaze Beta
1M
API model ID
DeepSeek V3
Not sourced
Interfaze Beta
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
DeepSeek V3
$0.07 per 1M cached input tokens
Interfaze Beta
Not published
Documented inputs
DeepSeek V3
Not sourced
Interfaze Beta
Not sourced
Documented outputs
DeepSeek V3
Not sourced
Interfaze Beta
Not sourced
Provider availability
DeepSeek V3
Not sourced
Interfaze Beta
Not sourced
Reasoning profile
DeepSeek V3
Non-Reasoning
Interfaze Beta
Reasoning
Weight access
DeepSeek V3
Open Weight
Interfaze Beta
Proprietary
License
DeepSeek V3
Open Weight
Interfaze Beta
Proprietary
Release date
DeepSeek V3
2024-12-26
Interfaze Beta
2026-05-11
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- Repository review: $0.0168 vs $0.0855. Cache-heavy agent loop: $0.0304 vs $0.365.
- Context tradeoff
- Interfaze Beta has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, DeepSeek V3 or Interfaze Beta?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, DeepSeek V3 or Interfaze Beta?
Interfaze Beta is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, DeepSeek V3 or Interfaze Beta?
Interfaze Beta is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, DeepSeek V3 or Interfaze Beta?
For the stated presets, chat costs $0.00082 on DeepSeek V3 and $0.00325 on Interfaze Beta; repository review costs $0.0168 and $0.0855; the cache-heavy agent loop costs $0.0304 and $0.365. DeepSeek V3 does not fit this workload in one request. Interfaze Beta has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, DeepSeek V3 or Interfaze Beta?
Interfaze Beta has the larger documented context window: 1M, compared with 128K.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
DeepSeek V3
API / mo$1,028
Self-host / mo$18,221
Break-even1.2B/day
Interfaze Beta
API / mo$3,750
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence14 rows
Coding
Multimodal
Knowledge
Instruction following
Math
FrontierMath v2 (Tiers 1-3)
DeepSeek V31.724%SourceInterfaze Beta—Not directly comparable
14 public results · 1 shared
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Last updated September 24, 2026