Agentic
Not comparable- DeepSeek LLM 2.0
- Not ranked
- GPT-6 Luna
- 56.7
- Estimated · #35/157
- Basis
- BenchAlign lane · 0 vs 1 public rows
- Reading
- Not comparable
DeepSeek LLM 2.0 vs GPT-6 Luna
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.
Updated September 22, 2026. Rank says GPT-6 Luna is ahead. Price, access, and your workload can each overturn that. 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
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.
Long documents
Prompts that approach the documented context limit
GPT-6 Luna
GPT-6 Luna has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
DeepSeek LLM 2.0 is 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 LLM 2.0 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
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 LLM 2.0 does not fit this workload in one request. DeepSeek LLM 2.0 has no comparable published API token rate.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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.
—DeepSeek LLM 2.066.6GPT-6 Luna
Not comparable · BenchAlign
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.
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
7 GPT-6 Luna only
- Shared results
- 0
- DeepSeek LLM 2.0 only
- 0
- GPT-6 Luna only
- 7
- 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 LLM 2.0
- Not ranked
- GPT-6 Luna
- 66.6
- Estimated · #11/159
- Basis
- BenchAlign lane · 0 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- DeepSeek LLM 2.0
- Not ranked
- GPT-6 Luna
- 78.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- DeepSeek LLM 2.0
- Not ranked
- GPT-6 Luna
- 71.3
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- DeepSeek LLM 2.0
- Not ranked
- GPT-6 Luna
- 63.6
- Estimated · #29/189
- Basis
- BenchAlign lane · 0 vs 5 public rows
- Reading
- Not comparable
Multilingual
Not comparable- DeepSeek LLM 2.0
- Not ranked
- GPT-6 Luna
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- DeepSeek LLM 2.0
- Not ranked
- GPT-6 Luna
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- DeepSeek LLM 2.0
- Not ranked
- GPT-6 Luna
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | DeepSeek LLM 2.0 | GPT-6 Luna | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | 56.7Estimated · #35/157 | Not comparableBenchAlign lane · 0 vs 1 public rows | Not comparable |
| Coding | Not ranked | 66.6Estimated · #11/159 | Not comparableBenchAlign lane · 0 vs 1 public rows | Not comparable |
| Reasoning | Not ranked | 78.3Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 71.3Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | Not ranked | 63.6Estimated · #29/189 | Not comparableBenchAlign lane · 0 vs 5 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 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) 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 LLM 2.0
- Self-hosted; infrastructure cost varies
- Fits in one request
- GPT-6 Luna
- $0.00035
- Fits in one request
DeepSeek LLM 2.0 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- DeepSeek LLM 2.0
- Self-hosted; infrastructure cost varies
- Fits in one request
- GPT-6 Luna
- $0.0065
- Fits in one request
DeepSeek LLM 2.0 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- DeepSeek LLM 2.0
- Self-hosted; infrastructure cost varies
- Does not fit in one request
- Cached-input rate unavailable
- GPT-6 Luna
- $0.009
- Fits in one request
DeepSeek LLM 2.0 does not fit this workload in one request. DeepSeek LLM 2.0 has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationDeepSeek LLM 2.0GPT-6 Luna
Context window
Maximum documented context; output-token limits may be lower.
DeepSeek LLM 2.0
128K
GPT-6 Luna
API model ID
DeepSeek LLM 2.0
Not sourced
GPT-6 Luna
gpt-6-luna
OpenAI GPT-6 Luna model documentationCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
DeepSeek LLM 2.0
No comparable hosted API rate
GPT-6 Luna
$0.01 per 1M cached input tokens
OpenAI GPT-6 Luna model documentationDocumented inputs
DeepSeek LLM 2.0
Not sourced
GPT-6 Luna
text, image
OpenAI GPT-6 Luna model documentationDocumented outputs
DeepSeek LLM 2.0
Not sourced
GPT-6 Luna
Provider availability
DeepSeek LLM 2.0
Not sourced
GPT-6 Luna
Generally Available · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Work, Codex
OpenAI GPT-6 Sol and Luna launchReasoning profile
DeepSeek LLM 2.0
Non-Reasoning
GPT-6 Luna
Reasoning
Weight access
DeepSeek LLM 2.0
Open Weight
GPT-6 Luna
Proprietary
License
DeepSeek LLM 2.0
Open Weight
GPT-6 Luna
Proprietary
Release date
DeepSeek LLM 2.0
Not sourced
GPT-6 Luna
2026-09-16
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
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- GPT-6 Luna has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence7 rows
Agentic
ExploitGym
DeepSeek LLM 2.0—GPT-6 Luna11.6%SourceNot directly comparable
Coding
DeepSWE
DeepSeek LLM 2.0—GPT-6 Luna66.6%SourceNot directly comparable
Knowledge
HealthBench (raw)
DeepSeek LLM 2.0—GPT-6 Luna50.0%SourceNot directly comparable
HealthBench (length-adjusted)
DeepSeek LLM 2.0—GPT-6 Luna54.5%SourceNot directly comparable
HealthBench Professional
DeepSeek LLM 2.0—GPT-6 Luna60.8%SourceNot directly comparable
HealthBench Professional (raw)
DeepSeek LLM 2.0—GPT-6 Luna61.2%SourceNot directly comparable
HealthBench Hard
DeepSeek LLM 2.0—GPT-6 Luna31.4%SourceNot directly comparable
Questions
Which is better, DeepSeek LLM 2.0 or GPT-6 Luna?
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 LLM 2.0 or GPT-6 Luna?
DeepSeek LLM 2.0 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, DeepSeek LLM 2.0 or GPT-6 Luna?
DeepSeek LLM 2.0 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, DeepSeek LLM 2.0 or GPT-6 Luna?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, DeepSeek LLM 2.0 or GPT-6 Luna?
GPT-6 Luna has the larger documented context window: 1.05M, compared with 128K.
Related comparisons
Last updated September 22, 2026
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