DeepSeek V3.2 vs Mistral Small 4

Summary (12 tests): DeepSeek V3.2 wins 6 tests, Mistral Small 4 wins 1, and 5 tests tie in our 12-test suite. Detailed walk-through (scores shown are from our 1–5 internal scale and ranks use our model rankings): - Strategic analysis: DeepSeek 5 vs Mistral 4. DeepSeek is tied for 1st ("tied for 1st with 25 others") while Mistral ranks 27 of 54. This means DeepSeek produces stronger nuanced tradeoff reasoning for numeric/strategic tasks. - Constrained rewriting: DeepSeek 4 (rank 6 of 53) vs Mistral 3 (rank 31). DeepSeek handles tight-length rewrites more reliably. - Faithfulness: DeepSeek 5 (tied for 1st) vs Mistral 4 (rank 34). In our tests DeepSeek sticks to source material more consistently; expect fewer hallucinations on factual transforms. - Classification: DeepSeek 3 (rank 31 of 53) vs Mistral 2 (rank 51 of 53). DeepSeek is noticeably better at routing/categorization in our suite. - Long context: DeepSeek 5 (tied for 1st) vs Mistral 4 (rank 38). Despite Mistral’s larger raw context window (262,144 vs DeepSeek’s 163,840), DeepSeek scored higher on retrieval accuracy at 30K+ tokens in our tests. - Agentic planning: DeepSeek 5 (tied for 1st) vs Mistral 4 (rank 16). DeepSeek decomposes goals and plans recovery paths more effectively in our scenarios. - Tool calling: Mistral 4 vs DeepSeek 3. Mistral’s higher score (rank 18 of 54 vs DeepSeek rank 47) indicates better function selection, argument accuracy and sequencing in our tool-calling tests. If your product relies on precise tool orchestration, Mistral has the edge. - Ties (both models scored the same): structured_output (both 5, tied for 1st), creative_problem_solving (both 4, rank 9), safety_calibration (both 2, rank 12), persona_consistency (both 5, tied for 1st), multilingual (both 5, tied for 1st). These ties mean both models are equivalent on schema adherence, ideation quality, basic safety refusals, persona maintenance and non-English quality in our suite. Additional context from the payload: Mistral supports text+image→text modality (useful for vision-and-language tasks) and has a larger context window (262,144). DeepSeek lists extra supported parameters such as logprobs and logit_bias in the payload; Mistral’s parameter list omits those fields. In short, DeepSeek wins most analytic and faithfulness-oriented tests in our benchmarks; Mistral’s principal advantage in our suite is tool calling and multimodal input support.

Costs from the payload: DeepSeek V3.2 input $0.26/million tokens and output $0.38/million; Mistral Small 4 input $0.15/million and output $0.60/million. Example scenarios (using payload rates): - Equal split (1M total tokens, 50% input/50% output): DeepSeek = $0.32 per 1M tokens; Mistral = $0.375 per 1M. Gap = $0.055 per 1M → $0.55 at 10M, $5.50 at 100M. - Output-heavy (1M total tokens, 20% input/80% output): DeepSeek = $0.356 per 1M; Mistral = $0.51 per 1M. Gap = $0.154 per 1M → $1.54 at 10M, $15.40 at 100M. - Symmetric 1M input + 1M output (2M tokens billed): DeepSeek = $0.64; Mistral = $0.75. Gap = $0.11 per paired Mtok → $1.10 at 10 pairs, $11.00 at 100 pairs. Who should care: high-volume output-heavy deployments (chatbots with long replies, batch generation) will feel Mistral’s higher output price the most; small-volume or research use won’t be materially affected by the $0.05–$0.15/Mtok differences.

Choose DeepSeek V3.2 if you need: - Best results on long-context retrieval (5/5 in our tests) and faithfulness (5/5), strategic analysis, and agentic planning; - Strong constrained rewriting and classification; - Slightly lower cost for many usage mixes (see pricing). Choose Mistral Small 4 if you need: - Better tool calling (4/5 in our tests) and tighter function selection/argument sequencing; - Native text+image→text modality or the largest raw context window (262,144) for multimodal inputs; - You accept a higher output cost for gains in tool orchestration.