Updated November 13, 2026. Rank says Gemini 4 Argon is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Gemini 4 Argon has the higher public score, 79.03 versus 64.48, and the 90% score intervals do not overlap. 16 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
DeepSeek V4.1 Flash 1017: $0.30 input / $1.20 output
Gemini 4 Argon: $2 input / $10 output
DeepSeek V4.1 Flash 1017: 1M
Gemini 4 Argon: 1M
DeepSeek V4.1 Flash 1017: October 17, 2026
Gemini 4 Argon: September 30, 2026
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.
Code generation, repair, and software-engineering tasks
Gemini 4 Argon
Gemini 4 Argon has the higher public coding point estimate, 71.8 to 59.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
Gemini 4 Argon
Gemini 4 Argon has the higher public agentic point estimate, 74.9 to 61.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
1K fresh input + 500 output tokens
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
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.
Like-for-like · BenchAlign v5.8
Gemini 4 Argon has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.
We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
FrontierMath v2 (Tier 4)Math
Normalized gap 51.2FrontierMath v2 (Tiers 1-3)Math
Normalized gap 18.6AutomationBenchAgentic
Normalized gap 9.2Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, reasoning, multimodal, 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.
| Category | DeepSeek V4.1 Flash 1017 | Gemini 4 Argon | Basis | Reading |
|---|---|---|---|---|
| Agentic | 61.1Supported · #25/135 | 74.9Supported · #6/135 | Like-for-likeBenchAlign v5.8 lane · 12 vs 13 public rows | Gemini 4 Argon leads · intervals overlap |
| Coding | 59.7Supported · #18/162 | 71.8Supported · #9/162 | Like-for-likeBenchAlign v5.8 lane · 13 vs 9 public rows | Gemini 4 Argon leads · intervals overlap |
| Knowledge | 65.1Supported · #29/180 | 79.2Supported · #8/180 | Like-for-likeBenchAlign v5.8 lane · 3 vs 2 public rows | Gemini 4 Argon leads · intervals overlap |
| Reasoning | 59.5Estimated · #35/160 | 76.5Supported · #7/160 | Directional onlyBenchAlign v5.8 lane · 3 vs 7 public rows | Directional only |
| Multimodal | 64.7Supported · #21/67 | Not ranked | Not comparableBenchAlign v5.8 lane · 5 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 | 65.6Unranked · 3 rankable rows | 87.4Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 2 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, 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.
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.
1K fresh input + 500 output tokens
DeepSeek V4.1 Flash 1017 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
DeepSeek V4.1 Flash 1017 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
DeepSeek V4.1 Flash 1017 has the lower modeled cost
Costs use the listed standard API rates.
Enter a month of tokens to price it at both models’ listed API rates.
Standard listed rates, no batch or volume discounts.
Overall score against output price per 1M tokens, one model per family. Pricing as of November 12, 2026.
DeepSeek V4.1 Flash 1017Gemini 4 Argon
Gray dots are the other priced models. The dark step line is the efficiency frontier: for a model under it, another model costs no more and scores at least as high.
See DeepSeek V4.1 Flash 1017 on the full price vs performance chartSourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
DeepSeek V4.1 Flash 1017
Gemini 4 Argon
DeepSeek V4.1 Flash 1017
deepseek-flash
DeepSeek models and pricingGemini 4 Argon
gemini-4-argon
Google Gemini 4 Argon model documentationA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
DeepSeek V4.1 Flash 1017
$0.006 per 1M cached input tokens
DeepSeek: Models & PricingGemini 4 Argon
$0.1 per 1M cached input tokens
Google Gemini API pricingDeepSeek V4.1 Flash 1017
text, image
DeepSeek vision guideGemini 4 Argon
text, image, video, audio, pdf
Google Gemini 4 Argon model documentationDeepSeek V4.1 Flash 1017
Gemini 4 Argon
DeepSeek V4.1 Flash 1017
Generally Available · DeepSeek API, open weights
DeepSeek-V4.1-Flash-1017 API changelogGemini 4 Argon
Generally Available · Gemini API, Google AI Studio
Google Gemini 4 Argon model documentationDeepSeek V4.1 Flash 1017
Reasoning
Gemini 4 Argon
Reasoning
DeepSeek V4.1 Flash 1017
Open Weight
Gemini 4 Argon
Proprietary
DeepSeek V4.1 Flash 1017
Open Weight
Gemini 4 Argon
Proprietary
DeepSeek V4.1 Flash 1017
2026-10-17
Gemini 4 Argon
2026-09-30
Gemini 4 Argon has the higher public score, 79.03 versus 64.48, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Gemini 4 Argon has the higher public coding point estimate, 71.8 to 59.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Gemini 4 Argon has the higher public agentic tasks point estimate, 74.9 to 61.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.0009 on DeepSeek V4.1 Flash 1017 and $0.007 on Gemini 4 Argon; repository review costs $0.0186 and $0.13; the cache-heavy agent loop costs $0.0192 and $0.16. Costs use the listed standard API rates.
Both models list the same context window, 1M.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.1
Not directly comparable
terminalBench3
Not directly comparable
Terminal-Bench 4.0
Gemini 4 Argon leads this result
CyberGym
Not directly comparable
ExploitGym
Gemini 4 Argon leads this result
HLE w/ tools
Not directly comparable
AutomationBench
DeepSeek V4.1 Flash 1017 leads this result
Agents' Last Exam
Gemini 4 Argon leads this result
Terminal-Bench 2.1 (Vals)
DeepSeek V4.1 Flash 1017 leads this result
CWE-bench v1
Shared sourceGemini 4 Argon leads this result
MazeBench
Gemini 4 Argon leads this result
CWE-bench
Shared sourceGemini 4 Argon leads this result
Finance Agent v2
Not directly comparable
OSWorld 2.0
Not directly comparable
Terminal-Bench-Science 0.1 (6x verifier timeout)
Not directly comparable
OSWorld 2.1 Offline
Not directly comparable
RoboDojo Sim
Not directly comparable
ScarfBench
Shared sourceGemini 4 Argon leads this result
Codeforces
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
terminalBench3
Not directly comparable
DeepSWE
Gemini 4 Argon leads this result
ProgramBench
Not directly comparable
NL2Repo
Not directly comparable
OpenHarmony Bench v1.0
Not directly comparable
Bug Hunt Bench
Shared sourceGemini 4 Argon leads this result
FrontierCode 2.0
Shared sourceGemini 4 Argon leads this result
CursorBench 4.0
Shared sourceGemini 4 Argon leads this result
VulcanBench Frontier v4
Not directly comparable
OpenHarmony Bench v1.2
Not directly comparable
FrontierSWE v2
Not directly comparable
Vibe Code Bench
Not directly comparable
PostTrainBench v1.1
Not directly comparable
PostTrainBench
Not directly comparable
STEMBench
Shared sourceDeepSeek V4.1 Flash 1017 leads this result
JevBench 1.4
Not directly comparable
JevBench 1.5
Not directly comparable
Graphwalks BFS 128K
Not directly comparable
GraphWalks BFS 256K–1M
Not directly comparable
ARC-AGI-1
Not directly comparable
ARC-AGI-2
Not directly comparable
ARC-AGI-3
Not directly comparable
ARC-AGI-3 Provider Adapter
Not directly comparable
ExtractBench (LlamaIndex)
Not directly comparable
ParseBench-1
Not directly comparable
Chartography (tools)
Not directly comparable
BabyVision w/ Python
Not directly comparable
ZeroBench w/ Python
Not directly comparable
ParseBench-2
Not directly comparable
ExtractBench Pro
Not directly comparable
Chartography (no tools)
Not directly comparable
LVBench
Not directly comparable
Blueprint-Bench 2
Not directly comparable
GPQA
Not directly comparable
GPQA-D
Not directly comparable
HLE
Not directly comparable
LABBench2
Not directly comparable
Morphology-to-molecule matching
Not directly comparable
Gray Swan IPI (15 attempts)
Not directly comparable
Apex
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared sourceGemini 4 Argon leads this result
FrontierMath v2 (Tier 4)
Shared sourceGemini 4 Argon leads this result
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
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Last updated November 13, 2026
