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 51.9, and the 90% score intervals do not overlap. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Gemini 4 Argon: $2 input / $10 output
MiMo-V2.5-Pro: No comparable API price
Gemini 4 Argon: 1M
MiMo-V2.5-Pro: 1M
Gemini 4 Argon: September 30, 2026
MiMo-V2.5-Pro: April 22, 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 42.3, 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 42.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
Each 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 | Gemini 4 Argon | MiMo-V2.5-Pro | Basis | Reading |
|---|---|---|---|---|
| Agentic | 74.9Supported · #6/135 | 42.1Supported · #62/135 | Like-for-likeBenchAlign v5.8 lane · 13 vs 6 public rows | Gemini 4 Argon leads |
| Coding | 71.8Supported · #9/162 | 42.3Supported · #66/162 | Like-for-likeBenchAlign v5.8 lane · 9 vs 4 public rows | Gemini 4 Argon leads |
| Knowledge | 79.2Supported · #8/180 | 51.8Supported · #70/180 | Like-for-likeBenchAlign v5.8 lane · 2 vs 4 public rows | Gemini 4 Argon leads |
| Reasoning | 76.5Supported · #7/160 | 45.5Estimated · #68/160 | Directional onlyBenchAlign v5.8 lane · 7 vs 0 public rows | Directional only |
| Multimodal | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 5 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 93.8#8/157 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 87.4Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 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
MiMo-V2.5-Pro has no comparable published API token rate.
50K fresh input + 3K output tokens
MiMo-V2.5-Pro has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
MiMo-V2.5-Pro has no comparable published API token rate.
Overall score against output price per 1M tokens, one model per family. Pricing as of November 12, 2026.
Gemini 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 Gemini 4 Argon 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.
Gemini 4 Argon
MiMo-V2.5-Pro
1M
Gemini 4 Argon
gemini-4-argon
Google Gemini 4 Argon model documentationMiMo-V2.5-Pro
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 4 Argon
$0.1 per 1M cached input tokens
Google Gemini API pricingMiMo-V2.5-Pro
No comparable hosted API rate
Gemini 4 Argon
text, image, video, audio, pdf
Google Gemini 4 Argon model documentationMiMo-V2.5-Pro
Gemini 4 Argon
MiMo-V2.5-Pro
Gemini 4 Argon
Generally Available · Gemini API, Google AI Studio
Google Gemini 4 Argon model documentationMiMo-V2.5-Pro
Not sourced
Gemini 4 Argon
Reasoning
MiMo-V2.5-Pro
Reasoning
Gemini 4 Argon
Proprietary
MiMo-V2.5-Pro
Proprietary
Gemini 4 Argon
Proprietary
MiMo-V2.5-Pro
Proprietary
Gemini 4 Argon
2026-09-30
MiMo-V2.5-Pro
2026-04-22
Gemini 4 Argon has the higher public score, 79.03 versus 51.9, 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 42.3, 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 42.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Both models list the same context window, 1M.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
AutomationBench
Not directly comparable
Finance Agent v2
Not directly comparable
Terminal-Bench 4.0
Not directly comparable
Agents' Last Exam
Not directly comparable
OSWorld 2.0
Not directly comparable
CWE-bench v1
Not directly comparable
Terminal-Bench-Science 0.1 (6x verifier timeout)
Not directly comparable
MazeBench
Gemini 4 Argon leads this result
OSWorld 2.1 Offline
Not directly comparable
ExploitGym
Not directly comparable
RoboDojo Sim
Not directly comparable
CWE-bench
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 4 Argon leads this result
Claw-Eval
Not directly comparable
τ³-bench results
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Gert Labs
Not directly comparable
ScarfBench
Not directly comparable
DeepSWE
Not directly comparable
FrontierSWE v2
Not directly comparable
Vibe Code Bench
Not directly comparable
PostTrainBench v1.1
Not directly comparable
Bug Hunt Bench
Not directly comparable
FrontierCode 2.0
Not directly comparable
CursorBench 4.0
Not directly comparable
PostTrainBench
Not directly comparable
SWE-bench Pro
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
STEMBench
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
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
LABBench2
Not directly comparable
Morphology-to-molecule matching
Not directly comparable
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
Gray Swan IPI (15 attempts)
Not directly comparable
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Last updated November 13, 2026
