diff --git a/doc/release_notes.rst b/doc/release_notes.rst
index dac57907..e694a5c4 100644
--- a/doc/release_notes.rst
+++ b/doc/release_notes.rst
@@ -6,6 +6,8 @@ Upcoming Version
 
 **Bug Fixes**
 
+* Fix the parsing of solutions returned by the CBC solver when setting a MIP duality
+  gap tolerance.
 * Improve the mapping of termination conditions for the SCIP solver
 
 Version 0.5.3
diff --git a/linopy/solvers.py b/linopy/solvers.py
index 5ea3ffb4..2fb7cb8e 100644
--- a/linopy/solvers.py
+++ b/linopy/solvers.py
@@ -14,6 +14,7 @@
 import subprocess as sub
 import sys
 from abc import ABC, abstractmethod
+from collections import namedtuple
 from collections.abc import Generator
 from pathlib import Path
 from typing import TYPE_CHECKING, Any, Callable
@@ -438,20 +439,20 @@ def solve_problem_from_file(
             p.stdout.close()
             p.wait()
         else:
-            log_f = open(log_fn, "w")
-            p = sub.Popen(command.split(" "), stdout=log_f, stderr=log_f)
-            p.wait()
+            with open(log_fn, "w") as log_f:
+                p = sub.Popen(command.split(" "), stdout=log_f, stderr=log_f)
+                p.wait()
 
         with open(solution_fn) as f:
-            data = f.readline()
+            first_line = f.readline()
 
-        if data.startswith("Optimal - objective value"):
+        if first_line.startswith("Optimal "):
             status = Status.from_termination_condition("optimal")
-        elif "Infeasible" in data:
+        elif "Infeasible" in first_line:
             status = Status.from_termination_condition("infeasible")
         else:
             status = Status(SolverStatus.warning, TerminationCondition.unknown)
-        status.legacy_status = data
+        status.legacy_status = first_line
 
         # Use HiGHS to parse the problem file and find the set of variable names, needed to parse solution
         h = highspy.Highs()
@@ -459,7 +460,11 @@ def solve_problem_from_file(
         variables = {v.name for v in h.getVariables()}
 
         def get_solver_solution() -> Solution:
-            objective = float(data[len("Optimal - objective value ") :])
+            m = re.match(r"Optimal.* - objective value (\d+\.?\d*)$", first_line)
+            if m and len(m.groups()) == 1:
+                objective = float(m.group(1))
+            else:
+                objective = np.nan
 
             with open(solution_fn, "rb") as f:
                 trimmed_sol_fn = re.sub(rb"\*\*\s+", b"", f.read())
@@ -482,7 +487,20 @@ def get_solver_solution() -> Solution:
         solution = self.safe_get_solution(status=status, func=get_solver_solution)
         solution = maybe_adjust_objective_sign(solution, io_api, sense)
 
-        return Result(status, solution)
+        # Parse the output and get duality gap and solver runtime
+        mip_gap, runtime = None, None
+        if log_fn is not None:
+            with open(log_fn) as log_f:
+                output = "".join(log_f.readlines())
+        m = re.search(r"\nGap: +(\d+\.?\d*)\n", output)
+        if m and len(m.groups()) == 1:
+            mip_gap = float(m.group(1))
+        m = re.search(r"\nTime \(Wallclock seconds\): +(\d+\.?\d*)\n", output)
+        if m and len(m.groups()) == 1:
+            runtime = float(m.group(1))
+        CbcModel = namedtuple("CbcModel", ["mip_gap", "runtime"])
+
+        return Result(status, solution, CbcModel(mip_gap, runtime))
 
 
 class GLPK(Solver):