Update README.md

README.md

@@ -77,7 +77,8 @@ All models are tested at full BF16 precision with maximum effort levels.
 
 Each lab typically only provides a limited subset of available benchmarks.
 Our table below shows official model card scores where available, and we use our own in-house eval flow for missing evals (italized).
-In cases where the harness is not specified, we use OpenCode.
+For SWE-bench Pro v2.0 Hard and DeepSWE 1.1, we use Terminus 2 for Ripple-65B Preview.
+In other cases where the harness is not specified, we use OpenCode.
 A higher number is better for all benchmarks.
 
 Replicating benchmark scores has been challenging, particularly for modern agentic evals, due to the number of factors which can cause 
@@ -111,7 +112,7 @@ Therefore, our results represent a best-effort attempt at a fair comparison.
   | SWE-bench Pro v1.0 | 61.42 | *46.9* | 54.3 | *58.2* | **66.1** |
   | SWE-bench Pro v2.0 Hard | **79.1** | *30.7* | *2.0* | *52.3* | 76.5 |
   | DeepSWE 1.1 | 44.2 | *0.2* | *1.8* | **60.0** | 43.3 |
-  | SciCode | 39.4 | 44.6 | *47.0* | **58.5** | 49.2 | 
+  | SciCode (Inspect AI) | 39.4 | 44.6 | *47.0* | **58.5** | 49.2 | 
   | τ³-bench banking | 31.27 | 22.6 | 23.7 | *14.4* | **36.1** |
   | AutomationBench (Public) | 36.4 | *8.4* | *14.6* | *42.4* | 39.3 | 
   | MCP Atlas | 71.67 | *62.2* | 76.0 | **89.2** | 80.8 |