Agentic
Like-for-like- Gemini 3 Flash
- 34.0
- Supported · #134/152
- Mistral Medium 3.5 128B
- 21.9
- Supported · #150/152
- Basis
- BenchAlign lane · 4 vs 3 public rows
- Reading
- Gemini 3 Flash leads · intervals overlap
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Follow model changesUpdated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Gemini 3 Flash has the higher public score, 62.2 versus 30.13, and the 90% score intervals do not overlap.
5 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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 Flash
Gemini 3 Flash leads on the public agentic lane, 34 to 21.9, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Prompts that approach the documented context limit
Gemini 3 Flash
Gemini 3 Flash has the larger documented context window.
Confidence: documented
1K fresh input + 500 output tokens
Gemini 3 Flash
Gemini 3 Flash 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 Flash
Gemini 3 Flash 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.
Confidence: rate-fallback
50K fresh input + 3K output tokens
Gemini 3 Flash
Gemini 3 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-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.
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 Flash | Mistral Medium 3.5 128B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 34.0Supported · #134/152 | 21.9Supported · #150/152 | Like-for-likeBenchAlign lane · 4 vs 3 public rows | Gemini 3 Flash leads · intervals overlap |
| Knowledge | 56.9Supported · #45/183 | 39.0Supported · #140/183 | Like-for-likeBenchAlign lane · 2 vs 2 public rows | Gemini 3 Flash leads · intervals overlap |
| Coding | 41.5Supported · #108/151 | 36.9Estimated · #127/151 | Directional onlyBenchAlign lane · 3 vs 2 public rows | Directional only |
| Instruction following | 66.2#68/123 | 84.0#48/123 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 59.0Unranked · 2 rankable rows | 68.6Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 50.2Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 75.8Unranked · 1 rankable row | 55.6Unranked · 1 rankable row | 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.
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
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 Flash has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Gemini 3 Flash has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Gemini 3 Flash 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 Flash
Mistral Medium 3.5 128B
256K
Gemini 3 Flash
gemini-3-flash-preview
Google Gemini API pricingMistral 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 Flash
$0.05 per 1M cached input tokens
Google Gemini API pricingMistral Medium 3.5 128B
Not published
Gemini 3 Flash
Not sourced
Mistral Medium 3.5 128B
Not sourced
Gemini 3 Flash
Not sourced
Mistral Medium 3.5 128B
Not sourced
Gemini 3 Flash
Not sourced
Mistral Medium 3.5 128B
Not sourced
Gemini 3 Flash
Non-Reasoning
Mistral Medium 3.5 128B
Reasoning
Gemini 3 Flash
Proprietary
Mistral Medium 3.5 128B
Open Weight
Gemini 3 Flash
Proprietary
Mistral Medium 3.5 128B
Open Weight
Gemini 3 Flash
2025-12-01
Mistral Medium 3.5 128B
2026-04-29
Run the same representative tasks against both endpoints before changing production traffic.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Claw-Eval
Not directly comparable
Gert Labs
Shared sourceGemini 3 Flash leads this result
JobBench
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3 Flash leads this result
τ³-bench results
Not directly comparable
Vibe Code Bench
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Gemini 3 Flash leads this result
SWE-bench Verified
Not directly comparable
Gemini 3 Flash has the higher public score, 62.2 versus 30.13, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Gemini 3 Flash scores higher for coding on the public lane, 41.5 to 36.9. 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 Flash leads the public agentic tasks lane, 34 to 21.9, with Supported evidence for both models, although the 90% intervals overlap.
For the stated presets, chat costs $0.002 on Gemini 3 Flash and $0.00525 on Mistral Medium 3.5 128B; repository review costs $0.034 and $0.0975; the cache-heavy agent loop costs $0.05 and $0.405. Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate.
Gemini 3 Flash has the larger documented context window: 1M, compared with 256K.
Last updated September 10, 2026
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