Model comparison
Gemini 1.5 Pro vs Llama 3.1 405B
Head-to-head evidence from 4 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Gemini 1.5 Pro #185 (Supported); Llama 3.1 405B #102 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Gemini 1.5 Pro and Llama 3.1 405B share 4 comparable benchmark results. 0 of 8 categories are comparable. 2 results are unique to Gemini 1.5 Pro; 7 to Llama 3.1 405B.
Updated July 20, 2026- Shared results
- 4
- Gemini 1.5 Pro only
- 2
- Llama 3.1 405B only
- 7
- Comparable categories
- 0 / 8
Benchmark data for Gemini 1.5 Pro and Llama 3.1 405B is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 4 shared benchmark results across 2 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
Gemini 1.5 Pro is priced at $1.25 input / $5.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Llama 3.1 405B. Gemini 1.5 Pro has the larger context window at 2M, compared with 128K for Llama 3.1 405B.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Gemini 1.5 Pro | Llama 3.1 405B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Gemini 1.5 Pro$1.25 input / $5 output | Llama 3.1 405B$0 input / $0 output | Llama 3.1 405B has the lower combined listed price. |
| Generation speedtokens per second | Gemini 1.5 ProNot available | Llama 3.1 405B29 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Gemini 1.5 ProNot available | Llama 3.1 405B2.19 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Gemini 1.5 Pro2M | Llama 3.1 405B128K | Gemini 1.5 Pro lists the larger context window. |
Benchmark Deep Dive
Agentic1 benchmarks
| Benchmark | Gemini 1.5 Pro | Llama 3.1 405B | Result |
|---|---|---|---|
| τ²-bench resultsSource | — | 19% | Not comparable |
Coding2 benchmarks
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | Gemini 1.5 Pro | Llama 3.1 405B | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 10.0% | 8.5% | Gemini 1.5 Pro leads |
| AA-GPQA DiamondSource | 58.9% | 51.5% | Gemini 1.5 Pro leads |
| AA-HLESource | 4.9% | 4.2% | Gemini 1.5 Pro leads |
| AA-Omniscience IndexSource | — | -17.3% | Not comparable |
| AA-Omniscience AccuracySource | — | 22.3% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 51.0% | Not comparable |
Multimodal1 benchmarks
| Benchmark | Gemini 1.5 Pro | Llama 3.1 405B | Result |
|---|---|---|---|
| AA-MMMU-ProSource | 55.0% | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Gemini 1.5 Pro | Llama 3.1 405B | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 39.0% | Not comparable |
Frequently Asked Questions (3)
Can I compare Gemini 1.5 Pro and Llama 3.1 405B on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for Gemini 1.5 Pro and Llama 3.1 405B today?
Gemini 1.5 Pro: $1.25 input / $5.00 output per 1M tokens Llama 3.1 405B: $0.00 input / $0.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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