Updated October 19, 2026. Rank says Gemini 3.5 Flash-Lite is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Gemini 3.5 Flash-Lite has the higher public point estimate, 49.61 versus 33.61. Their conditional score ranges overlap. These ranges do not establish rank confidence. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Mistral
33.61/100
Estimated · Public rank #165
Conditional range 19.3–48.0
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 3.5 Flash-Lite
Gemini 3.5 Flash-Lite has the higher public agentic point estimate, 35.6 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 3.5 Flash-Lite
Gemini 3.5 Flash-Lite has the larger documented context window.
1K fresh input + 500 output tokens
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite 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 3.5 Flash-Lite
Gemini 3.5 Flash-Lite 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
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite 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 3.5 Flash-Lite 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.
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.
MMLU-Pro (Vals)Knowledge
Normalized gap 10.5Each 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.
| Category | Gemini 3.5 Flash-Lite | Mistral Medium 3.5 128B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 35.6Supported · #81/130 | 19.9Supported · #111/130 | Like-for-likeBenchAlign v5.8 lane · 3 vs 3 public rows | Gemini 3.5 Flash-Lite leads |
| Knowledge | 49.1Supported · #86/186 | 31.8Supported · #150/186 | Like-for-likeBenchAlign v5.8 lane · 2 vs 2 public rows | Gemini 3.5 Flash-Lite leads · intervals overlap |
| Coding | 35.8Supported · #77/151 | 22.7Estimated · #115/151 | Directional onlyBenchAlign v5.8 lane · 4 vs 2 public rows | Directional only |
| Reasoning | 70.9Unranked · 3 rankable rows | 72.6Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | 80.9Unranked · 1 rankable row | 60.1Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 84.0#52/148 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 37.8Unranked · 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, 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
Gemini 3.5 Flash-Lite has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Gemini 3.5 Flash-Lite has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Gemini 3.5 Flash-Lite has the lower modeled cost
Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate.
Sourced 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 3.5 Flash-Lite
Mistral Medium 3.5 128B
256K
Gemini 3.5 Flash-Lite
gemini-3.5-flash-lite
Google Gemini 3.5 Flash-Lite 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 3.5 Flash-Lite
$0.03 per 1M cached input tokens
Google Gemini API pricingMistral Medium 3.5 128B
Not published
Gemini 3.5 Flash-Lite
text, image, video, audio, pdf
Google Gemini 3.5 Flash-Lite model documentationMistral Medium 3.5 128B
Not sourced
Gemini 3.5 Flash-Lite
Mistral Medium 3.5 128B
Not sourced
Gemini 3.5 Flash-Lite
Generally Available · Gemini API, Google AI Studio
Google latest Gemini model guideMistral Medium 3.5 128B
Not sourced
Gemini 3.5 Flash-Lite
Reasoning
Mistral Medium 3.5 128B
Reasoning
Gemini 3.5 Flash-Lite
Proprietary
Mistral Medium 3.5 128B
Open Weight
Gemini 3.5 Flash-Lite
Proprietary
Mistral Medium 3.5 128B
Open Weight
Gemini 3.5 Flash-Lite
2026-07-21
Mistral Medium 3.5 128B
2026-04-29
Gemini 3.5 Flash-Lite has the higher public point estimate, 49.61 versus 33.61. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Gemini 3.5 Flash-Lite scores higher for coding on the public lane, 35.8 to 22.7. 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 3.5 Flash-Lite has the higher public agentic tasks point estimate, 35.6 to 19.9, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.00155 on Gemini 3.5 Flash-Lite and $0.00525 on Mistral Medium 3.5 128B; repository review costs $0.0225 and $0.0975; the cache-heavy agent loop costs $0.037 and $0.405. Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate.
Gemini 3.5 Flash-Lite 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.
Terminal-Bench 2.1
Not directly comparable
OSWorld-Verified
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.5 Flash-Lite leads this result
τ³-bench results
Not directly comparable
Gert Labs
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
SWE-bench Pro
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Gemini 3.5 Flash-Lite leads this result
SWE-bench Verified
Not directly comparable
MRCRv2
Not directly comparable
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Last updated October 19, 2026
