diff --git a/evalharness/cli.py b/evalharness/cli.py
index 91c472e..b6e7b44 100644
--- a/evalharness/cli.py
+++ b/evalharness/cli.py
@@ -621,6 +621,54 @@ def _cmd_eval_run(args) -> int:
 
     rows = []
     all_reports = []
+
+    def _flush_summaries():
+        """Rewrite summary.csv/xlsx from the benches finished SO FAR.
+
+        Called after every benchmark instead of only at the very end:
+        multi-hour runs (mmlu_pro 12k samples) leave the summary stale for
+        hours otherwise -- and a crashed run would leave the LAST run's
+        corpses in place instead of partial results."""
+        if not out_dir:
+            return
+        if all_reports:
+            try:
+                from evalharness.viz import render as _render
+
+                _render(all_reports, style='excel', out=f'{out_dir}/summary.xlsx')
+            except Exception as e:
+                print(f'excel export skipped: {type(e).__name__}: {str(e)[:80]}',
+                      file=sys.stderr)
+        if not rows:
+            return
+        import csv as _csv
+
+        with open(f'{out_dir}/summary.csv', 'w', newline='', encoding='utf-8') as f:
+            w = _csv.writer(f)
+            w.writerow(['benchmark', 'score', 'metric', 'num_samples',
+                        'time_h', 'time_s', 'extract_fail',
+                        'success_rate', 'latency_mean_s', 'output_tps', 'request_qps',
+                        'input_tokens_mean', 'output_tokens_mean', 'total_tokens',
+                        'ttft_mean_s', 'ttft_p90_s', 'ttft_p99_s',
+                        'tpot_mean_s', 'tpot_p90_s', 'tpot_p99_s',
+                        'categories'])
+            for r in rows:
+                perf = (r.get('groups') or {}).get('perf') or {}
+                cats = '; '.join(f'{g}={_f3(v)}'
+                                 for gname, gv in (r.get('groups') or {}).items()
+                                 if gname != 'perf'
+                                 for g, v in (gv or {}).items()
+                                 if isinstance(v, (int, float)))[:2000]
+                w.writerow([r['name'], _f3(r.get('value')), r['metric'], r.get('n', ''),
+                            r.get('hours', ''), r.get('secs', ''),
+                            r.get('extract_fail', 0)] +
+                           [perf.get(k, '') for k in (
+                               'success_rate', 'latency_mean_s', 'output_tps', 'request_qps',
+                               'input_tokens_mean', 'output_tokens_mean', 'total_tokens',
+                               'ttft_mean_s', 'ttft_p90_s', 'ttft_p99_s',
+                               'tpot_mean_s', 'tpot_p90_s', 'tpot_p99_s')] +
+                           [cats])
+
     run_started = _time.time()
     total_runs = len(args.datasets)
     model_spec = _compose_model_spec(args)
@@ -908,49 +956,17 @@ def _cmd_eval_run(args) -> int:
                     border_style='red', expand=False), justify='center')
             _print_benchmark_result(console, i + 1, total_runs, name,
                                     'failed', _time.time() - t0)
+        # incremental summary: partial results are visible (and survive a
+        # crash) after EVERY benchmark, not only when the whole run ends
+        _flush_summaries()
 
     if _shared_reporter is not None:
         _shared_reporter.close()
     if all_reports and out_dir:
-        try:
-            from evalharness.viz import render as _render
-
-            xb = _render(all_reports, style='excel',
-                         out=f'{out_dir}/summary.xlsx')
-            print(f'excel  -> {xb}', flush=True)
-        except Exception as e:
-            print(f'excel export skipped: {type(e).__name__}: {str(e)[:80]}',
-                  file=sys.stderr)
+        _flush_summaries()   # final state (benches already flushed per-bench)
+        print(f'excel  -> {out_dir}/summary.xlsx', flush=True)
     if rows:
         _print_summary_table(console, rows)
-        if out_dir:
-            import csv as _csv
-
-            with open(f'{out_dir}/summary.csv', 'w', newline='', encoding='utf-8') as f:
-                w = _csv.writer(f)
-                w.writerow(['benchmark', 'score', 'metric', 'num_samples',
-                            'time_h', 'time_s', 'extract_fail',
-                            'success_rate', 'latency_mean_s', 'output_tps', 'request_qps',
-                            'input_tokens_mean', 'output_tokens_mean', 'total_tokens',
-                            'ttft_mean_s', 'ttft_p90_s', 'ttft_p99_s',
-                            'tpot_mean_s', 'tpot_p90_s', 'tpot_p99_s',
-                            'categories'])
-                for r in rows:
-                    perf = (r.get('groups') or {}).get('perf') or {}
-                    cats = '; '.join(f'{g}={_f3(v)}'
-                                     for gname, gv in (r.get('groups') or {}).items()
-                                     if gname != 'perf'
-                                     for g, v in (gv or {}).items()
-                                     if isinstance(v, (int, float)))[:2000]
-                    w.writerow([r['name'], _f3(r.get('value')), r['metric'], r.get('n', ''),
-                                r.get('hours', ''), r.get('secs', ''),
-                                r.get('extract_fail', 0)] +
-                               [perf.get(k, '') for k in (
-                                   'success_rate', 'latency_mean_s', 'output_tps', 'request_qps',
-                                   'input_tokens_mean', 'output_tokens_mean', 'total_tokens',
-                                   'ttft_mean_s', 'ttft_p90_s', 'ttft_p99_s',
-                                   'tpot_mean_s', 'tpot_p90_s', 'tpot_p99_s')] +
-                               [cats])
 
     # artifacts notice: tell the user where everything landed (or how to save);
     # rich terminals get clickable file:// links (iTerm2/kitty/WezTerm/WT...)