Gemini 3 Flash Preview vs Llama 3.3 70B Instruct

Gemini 3 Flash Preview wins 9 benchmarks outright, ties 2, and loses 1 against Llama 3.3 70B Instruct in our 12-test suite.

Where Flash Preview dominates:

• Agentic planning: 5 vs 3. Flash Preview is tied for 1st with 14 other models; Llama ranks 42nd of 54. This gap translates directly to reliability in multi-step AI workflows — goal decomposition, failure recovery, and chaining tool calls all depend on this capability.
• Tool calling: 5 vs 4. Flash Preview ties for 1st with 16 others; Llama ranks 18th of 54. Both handle basic function calling, but Flash Preview's higher score reflects better argument accuracy and sequencing under complex conditions.
• Creative problem solving: 5 vs 3. Flash Preview ties for 1st among only 8 models (rank 1 of 54, tied with 7 others); Llama ranks 30th of 54. A meaningful gap for open-ended generation tasks.
• Strategic analysis: 5 vs 3. Flash Preview ties for 1st with 25 others; Llama ranks 36th of 54. Real tradeoff reasoning with numbers — relevant for business analysis, planning documents, and research synthesis.
• Faithfulness: 5 vs 4. Flash Preview ties for 1st with 32 others; Llama ranks 34th of 55. Flash Preview is more reliable at sticking to source material without hallucinating — important for RAG pipelines.
• Persona consistency: 5 vs 3. Flash Preview ties for 1st with 36 others; Llama ranks 45th of 53. A stark gap — Llama struggles to hold character and resist prompt injection across turns.
• Multilingual: 5 vs 4. Flash Preview ties for 1st with 34 others; Llama ranks 36th of 55.
• Structured output: 5 vs 4. Flash Preview ties for 1st with 24 others; Llama ranks 26th of 54.
• Constrained rewriting: 4 vs 3. Flash Preview ranks 6th of 53; Llama ranks 31st.

Ties:

• Classification and long context: Both score 4 and 5 respectively, both tied for 1st in those categories with the same groups of models. Neither has an edge here.

Where Llama 3.3 70B Instruct wins:

• Safety calibration: 2 vs 1. Llama ranks 12th of 55; Flash Preview ranks 32nd of 55. Flash Preview's score of 1 places it in the bottom quartile (p25 = 1) for this test — it over-refuses legitimate requests or under-refuses harmful ones relative to peers. This is the one category where Llama is demonstrably better.

External benchmarks (Epoch AI):

• On AIME 2025 (math olympiad), Flash Preview scores 92.8% (rank 5 of 23 models with this score), while Llama 3.3 70B Instruct scores just 5.1% (rank 23 of 23 — last). This is a massive gap in mathematical reasoning.
• On MATH Level 5 (competition math), Llama scores 41.6% — rank 14 of 14 (last among scored models). Flash Preview has no MATH Level 5 score in the payload.
• On SWE-bench Verified (real GitHub issue resolution), Flash Preview scores 75.4% (rank 3 of 12), placing it among the top coding models by this external measure. Llama has no SWE-bench score in the payload.

These external benchmarks reinforce the internal picture: Flash Preview is substantially stronger at reasoning-intensive tasks, while Llama 3.3 70B Instruct's math and coding capabilities lag significantly by third-party measures.

Gemini 3 Flash Preview costs $0.50/MTok input and $3.00/MTok output. Llama 3.3 70B Instruct costs $0.10/MTok input and $0.32/MTok output — making it 5x cheaper on input and 9.4x cheaper on output.

At real-world volumes, the gap compounds fast. At 1M output tokens/month: Flash Preview costs $3.00 vs Llama's $0.32 — a $2.68 difference, negligible for most teams. At 10M output tokens/month: $30.00 vs $3.20 — a $26.80 gap that starts to matter for startups. At 100M output tokens/month: $300 vs $32 — a $268/month difference that becomes a budget line item.

Developers running high-volume inference pipelines — chatbots, document processors, bulk classification — will find Llama 3.3 70B Instruct meaningfully cheaper, especially since both models tie on classification and long context in our tests. For lower-volume agentic or coding workloads where Flash Preview's stronger tool calling and planning scores translate to fewer retries and shorter chains, the premium is easier to justify.

Choose Gemini 3 Flash Preview if:

• You're building agentic systems or tool-calling pipelines — its scores of 5 on both (vs Llama's 3 and 4) mean more reliable execution with fewer failures.
• Your application uses RAG or summarization where faithfulness matters — Flash Preview scored 5 vs Llama's 4 in our tests.
• You need strong math or coding performance — 92.8% on AIME 2025 vs Llama's 5.1% (Epoch AI) is not a close race.
• Your context window needs are large — Flash Preview supports 1,048,576 tokens vs Llama's 131,072.
• You need multimodal input: Flash Preview accepts text, image, file, audio, and video; Llama is text-only.
• Output volume is modest enough that the 9.4x output cost premium ($3.00 vs $0.32/MTok) is acceptable.

Choose Llama 3.3 70B Instruct if:

• Cost is the primary constraint and you're running at high volume — at 100M output tokens/month, you save ~$268 vs Flash Preview.
• Your use case is classification, long-context retrieval, or bulk text processing — both models tie on these benchmarks, so there's no reason to pay more.
• Safety calibration is a hard requirement — Llama ranks 12th of 55 models on this test; Flash Preview ranks 32nd.
• You need parameters like logprobs, top_k, min_p, logit_bias, or repetition_penalty — these are available in Llama's API but not listed for Flash Preview in our data.
• You want a text-in/text-out pipeline without multimodal complexity.