Coding
Like-for-like- Gemini 3.5 Flash-Lite
- 43.6
- Supported · #121/183
- Mistral Large 3
- 26.0
- Supported · #176/183
- Basis
- BenchAlign lane · 4 vs 0 public rows
- Reading
- Gemini 3.5 Flash-Lite leads
Five or fewer confirmed AI changes, with original sources, on mornings when something changed.A free source-linked morning brief.
See the free Radar BriefUpdated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
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.
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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 3.5 Flash-Lite
Gemini 3.5 Flash-Lite leads on the public coding lane, 43.6 to 26, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Prompts that approach the documented context limit
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite has the larger documented context window.
Confidence: documented
1K fresh input + 500 output tokens
Mistral Large 3
Mistral Large 3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-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 Large 3 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
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.
Confidence: listed-rates
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemini 3.5 Flash-Lite and Mistral Large 3 are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
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.
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.
| Category | Gemini 3.5 Flash-Lite | Mistral Large 3 | Basis | Reading |
|---|---|---|---|---|
| Coding | 43.6Supported · #121/183 | 26.0Supported · #176/183 | Like-for-likeBenchAlign lane · 4 vs 0 public rows | Gemini 3.5 Flash-Lite leads |
| Agentic | 43.4Estimated · #104/151 | 42.8Estimated · #111/151 | Directional onlyBenchAlign lane · 3 vs 0 public rows | Directional only |
| Knowledge | 53.0Supported · #71/181 | 43.8Estimated · #124/181 | Directional onlyBenchAlign lane · 2 vs 0 public rows | Directional only |
| Reasoning | 60.8Unranked · 3 rankable rows | 46.0Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | 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 |
| Multimodal | 76.1#16/48 | 41.8Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 41.4#100/120 | 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.
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.
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 Large 3 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 Large 3 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 Large 3
256K
Gemini 3.5 Flash-Lite
gemini-3.5-flash-lite
Google Gemini 3.5 Flash-Lite model documentationMistral Large 3
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 Large 3
Not published
Gemini 3.5 Flash-Lite
text, image, video, audio, pdf
Google Gemini 3.5 Flash-Lite model documentationMistral Large 3
Not sourced
Gemini 3.5 Flash-Lite
Mistral Large 3
Not sourced
Gemini 3.5 Flash-Lite
Generally Available · Gemini API, Google AI Studio
Google latest Gemini model guideMistral Large 3
Not sourced
Gemini 3.5 Flash-Lite
Reasoning
Mistral Large 3
Non-Reasoning
Gemini 3.5 Flash-Lite
Proprietary
Mistral Large 3
Proprietary
Gemini 3.5 Flash-Lite
Proprietary
Mistral Large 3
Proprietary
Gemini 3.5 Flash-Lite
2026-07-21
Mistral Large 3
2025-12-02
Run the same representative tasks against both endpoints before changing production traffic.
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 2.0
Not directly comparable
SWE-bench Pro
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
MRCRv2
Not directly comparable
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.
Gemini 3.5 Flash-Lite leads the public coding lane, 43.6 to 26, with Supported evidence for both models and non-overlapping 90% intervals.
Gemini 3.5 Flash-Lite scores higher for agentic tasks on the public lane, 43.4 to 42.8. Gemini 3.5 Flash-Lite and Mistral Large 3 are scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
For the stated presets, chat costs $0.00155 on Gemini 3.5 Flash-Lite and $0.00125 on Mistral Large 3; repository review costs $0.0225 and $0.0295; the cache-heavy agent loop costs $0.037 and $0.125. Mistral Large 3 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.
Last updated September 4, 2026
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