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 point estimate, 79.03 versus 28.19. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Mistral
28.19/100
Estimated · Public rank #191
Conditional range 13.8–42.5
Gemini 4 Argon: $2 input / $10 output
Mistral Medium 3.5 128B: $1.50 input / $7.50 output
Gemini 4 Argon: 1M
Mistral Medium 3.5 128B: 256K
Gemini 4 Argon: September 30, 2026
Mistral Medium 3.5 128B: April 29, 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.
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 19.9, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
Gemini 4 Argon
Gemini 4 Argon has the larger documented context window.
1K fresh input + 500 output tokens
Mistral Medium 3.5 128B
Mistral Medium 3.5 128B 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
Gemini 4 Argon
Gemini 4 Argon has the lower estimated token cost for this stated workload. Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate.
50K fresh input + 3K output tokens
Mistral Medium 3.5 128B
Mistral Medium 3.5 128B has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Mistral Medium 3.5 128B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
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.
Directional only · 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.
2 categories rest 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 | Mistral Medium 3.5 128B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 74.9Supported · #6/135 | 19.9Supported · #118/135 | Like-for-likeBenchAlign v5.8 lane · 13 vs 3 public rows | Gemini 4 Argon leads |
| Knowledge | 79.2Supported · #8/180 | 28.4Supported · #150/180 | Like-for-likeBenchAlign v5.8 lane · 2 vs 2 public rows | Gemini 4 Argon leads |
| Coding | 71.8Supported · #9/162 | 19.5Estimated · #135/162 | Directional onlyBenchAlign v5.8 lane · 9 vs 2 public rows | Directional only |
| Reasoning | 76.5Supported · #7/160 | 22.7Estimated · #143/160 | 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 | 84.0#54/157 | 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
Mistral Medium 3.5 128B has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Mistral Medium 3.5 128B has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Gemini 4 Argon has the lower modeled cost
Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate.
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.
Gemini 4 ArgonMistral Medium 3.5 128B
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 Mistral Medium 3.5 128B 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
Mistral Medium 3.5 128B
256K
Gemini 4 Argon
gemini-4-argon
Google Gemini 4 Argon model documentationMistral Medium 3.5 128B
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 pricingMistral Medium 3.5 128B
Not published
Gemini 4 Argon
text, image, video, audio, pdf
Google Gemini 4 Argon model documentationMistral Medium 3.5 128B
text, image
Mistral-Medium-3.5-128B model cardGemini 4 Argon
Mistral Medium 3.5 128B
Gemini 4 Argon
Generally Available · Gemini API, Google AI Studio
Google Gemini 4 Argon model documentationMistral Medium 3.5 128B
Not sourced
Gemini 4 Argon
Reasoning
Mistral Medium 3.5 128B
Reasoning
Gemini 4 Argon
Proprietary
Mistral Medium 3.5 128B
Open Weight
Gemini 4 Argon
Proprietary
Mistral Medium 3.5 128B
Open Weight
Gemini 4 Argon
2026-09-30
Mistral Medium 3.5 128B
2026-04-29
Gemini 4 Argon has the higher public point estimate, 79.03 versus 28.19. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Gemini 4 Argon scores higher for coding on the public lane, 71.8 to 19.5. Mistral Medium 3.5 128B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Gemini 4 Argon has the higher public agentic tasks point estimate, 74.9 to 19.9, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.007 on Gemini 4 Argon and $0.00525 on Mistral Medium 3.5 128B; repository review costs $0.13 and $0.0975; the cache-heavy agent loop costs $0.16 and $0.405. Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate.
Gemini 4 Argon has the larger documented context window: 1M, compared with 256K.
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.
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
Not directly comparable
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
τ³-bench results
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 Verified
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
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
