Model comparison
Llama 4 Scout vs SWE-1.7
Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Llama 4 Scout #171 (Supported); SWE-1.7 unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Llama 4 Scout and SWE-1.7 share 0 comparable benchmark results. 0 of 8 categories are comparable. 19 results are unique to Llama 4 Scout; 4 to SWE-1.7.
Updated July 17, 2026- Shared results
- 0
- Llama 4 Scout only
- 19
- SWE-1.7 only
- 4
- Comparable categories
- 0 / 8
Benchmark data for Llama 4 Scout and SWE-1.7 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 0 shared benchmark results across 0 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.
Llama 4 Scout has the larger context window at 10M, compared with 256K for SWE-1.7.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Llama 4 Scout | SWE-1.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Llama 4 Scout$0 input / $0 output | SWE-1.7Not available | A complete price comparison is not available. |
| Generation speedtokens per second | Llama 4 Scout128 tok/s | SWE-1.7Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Llama 4 Scout0.70 s | SWE-1.7Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Llama 4 Scout10M | SWE-1.7256K | Llama 4 Scout lists the larger context window. |
Benchmark Deep Dive
Agentic5 benchmarks
Coding6 benchmarks
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | Llama 4 Scout | SWE-1.7 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 10.0% | — | Not comparable |
| AA-GPQA DiamondSource | 58.7% | — | Not comparable |
| AA-HLESource | 4.3% | — | Not comparable |
| AA-Omniscience IndexSource | -52.4% | — | Not comparable |
| AA-Omniscience AccuracySource | 14.6% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 78.3% | — | Not comparable |
Math1 benchmarks
| Benchmark | Llama 4 Scout | SWE-1.7 | Result |
|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)Source | 0.000% | — | Not comparable |
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | Llama 4 Scout | SWE-1.7 | Result |
|---|---|---|---|
| AA-IFBenchSource | 39.5% | — | Not comparable |
Frequently Asked Questions (3)
Can I compare Llama 4 Scout and SWE-1.7 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 Llama 4 Scout and SWE-1.7 today?
Llama 4 Scout: $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.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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