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
DeepSeek V3 vs GPT-4.5
Updated October 14, 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. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 2
- DeepSeek V3 only
- 7
- GPT-4.5 only
- 4
- 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.
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
GPT-4.5 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
GPT-4.5 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documentedCache-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. GPT-4.5 does not fit this workload in one request.
Confidence: listed-rates
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.
15.1DeepSeek V3—GPT-4.5
Not comparable · BenchAlign v5.8
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.
7 DeepSeek V3 only
2 Shared
4 GPT-4.5 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.
SimpleQAKnowledge
Normalized gap 37.6DeepSeek V3:24.9%GPT-4.5:62.5%GPQAKnowledge
Normalized gap 10.4DeepSeek V3:59.1%GPT-4.5:69.5%
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 v5.8 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
- 10.0
- Estimated · #118/124
- GPT-4.5
- Not ranked
- Basis
- BenchAlign v5.8 lane · 0 vs 0 public rows
- Reading
- Not comparable
Coding
Not comparable- DeepSeek V3
- 15.1
- Estimated · #134/149
- GPT-4.5
- Not ranked
- Basis
- BenchAlign v5.8 lane · 2 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- DeepSeek V3
- 26.3
- Unranked · 3 rankable rows
- GPT-4.5
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- DeepSeek V3
- Not ranked
- GPT-4.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- DeepSeek V3
- 32.3
- Supported · #143/181
- GPT-4.5
- Not ranked
- Basis
- BenchAlign v5.8 lane · 3 vs 3 public rows
- Reading
- Not comparable
Multilingual
Not comparable- DeepSeek V3
- Not ranked
- GPT-4.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- DeepSeek V3
- 39.6
- #105/128
- GPT-4.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- DeepSeek V3
- Not ranked
- GPT-4.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | DeepSeek V3 | GPT-4.5 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 10.0Estimated · #118/124 | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows | Not comparable |
| Coding | 15.1Estimated · #134/149 | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 1 public rows | Not comparable |
| Reasoning | 26.3Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 32.3Supported · #143/181 | Not ranked | Not comparableBenchAlign v5.8 lane · 3 vs 3 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 39.6#105/128 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 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 v5.8) 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.
Supported evidence per lane · 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
- GPT-4.5
- $0.15
- 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
- GPT-4.5
- $4.20
- 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
- GPT-4.5
- $10.50
- Does not fit in one request
DeepSeek V3 does not fit this workload in one request. GPT-4.5 does not fit this workload in one request.
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 V3GPT-4.5
Context window
Maximum documented context; output-token limits may be lower.
DeepSeek V3
128K
GPT-4.5
API model ID
DeepSeek V3
Not sourced
GPT-4.5
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
GPT-4.5
$37.5 per 1M cached input tokens
OpenAI GPT-4.5 Preview model documentationDocumented inputs
DeepSeek V3
Not sourced
GPT-4.5
Not sourced
Documented outputs
DeepSeek V3
Not sourced
GPT-4.5
Not sourced
Provider availability
DeepSeek V3
Not sourced
GPT-4.5
Not sourced
Reasoning profile
DeepSeek V3
Non-Reasoning
GPT-4.5
Non-Reasoning
Weight access
DeepSeek V3
Open Weight
GPT-4.5
Proprietary
License
DeepSeek V3
Open Weight
GPT-4.5
Proprietary
Release date
DeepSeek V3
2024-12-26
GPT-4.5
2025-02-27
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 $4.20. Cache-heavy agent loop: $0.0304 vs $10.50.
- Context tradeoff
- Both models list 128K.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, DeepSeek V3 or GPT-4.5?
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 GPT-4.5?
GPT-4.5 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 GPT-4.5?
GPT-4.5 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, DeepSeek V3 or GPT-4.5?
For the stated presets, chat costs $0.00082 on DeepSeek V3 and $0.15 on GPT-4.5; repository review costs $0.0168 and $4.20; the cache-heavy agent loop costs $0.0304 and $10.50. DeepSeek V3 does not fit this workload in one request. GPT-4.5 does not fit this workload in one request.
Which has the larger context window, DeepSeek V3 or GPT-4.5?
Both models list the same context window, 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
GPT-4.5
API / mo$168,750
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 evidence13 rows
13 public results · 2 shared
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Last updated October 14, 2026