diff --git a/application/agents/base.py b/application/agents/base.py
index 8e6363fc..15735c8c 100644
--- a/application/agents/base.py
+++ b/application/agents/base.py
@@ -39,6 +39,7 @@ class BaseAgent(ABC):
         llm=None,
         llm_handler=None,
         tool_executor: Optional[ToolExecutor] = None,
+        backup_models: Optional[List[str]] = None,
     ):
         self.endpoint = endpoint
         self.llm_name = llm_name
@@ -63,6 +64,7 @@ class BaseAgent(ABC):
                 decoded_token=decoded_token,
                 model_id=model_id,
                 agent_id=agent_id,
+                backup_models=backup_models,
             )
 
         self.retrieved_docs = retrieved_docs or []
diff --git a/application/api/answer/services/stream_processor.py b/application/api/answer/services/stream_processor.py
index f7ee03d6..28a2e8d4 100644
--- a/application/api/answer/services/stream_processor.py
+++ b/application/api/answer/services/stream_processor.py
@@ -444,6 +444,7 @@ class StreamProcessor:
                     "user_api_key": effective_key,
                     "json_schema": data_key.get("json_schema"),
                     "default_model_id": data_key.get("default_model_id", ""),
+                    "models": data_key.get("models", []),
                 }
             )
 
@@ -816,6 +817,10 @@ class StreamProcessor:
         from application.llm.handlers.handler_creator import LLMHandlerCreator
         from application.agents.tool_executor import ToolExecutor
 
+        # Compute backup models: agent's configured models minus the active one
+        agent_models = self.agent_config.get("models", [])
+        backup_models = [m for m in agent_models if m != self.model_id]
+
         llm = LLMCreator.create_llm(
             provider or settings.LLM_PROVIDER,
             api_key=system_api_key,
@@ -823,6 +828,7 @@ class StreamProcessor:
             decoded_token=self.decoded_token,
             model_id=self.model_id,
             agent_id=self.agent_id,
+            backup_models=backup_models,
         )
         llm_handler = LLMHandlerCreator.create_handler(
             provider if provider else "default"
diff --git a/application/llm/base.py b/application/llm/base.py
index 9a0f249e..c8e30b4f 100644
--- a/application/llm/base.py
+++ b/application/llm/base.py
@@ -16,22 +16,61 @@ class BaseLLM(ABC):
         agent_id=None,
         model_id=None,
         base_url=None,
+        backup_models=None,
     ):
         self.decoded_token = decoded_token
         self.agent_id = str(agent_id) if agent_id else None
         self.model_id = model_id
         self.base_url = base_url
         self.token_usage = {"prompt_tokens": 0, "generated_tokens": 0}
+        self._backup_models = backup_models or []
         self._fallback_llm = None
-        self._fallback_sequence_index = 0
 
     @property
     def fallback_llm(self):
-        """Lazy-loaded fallback LLM from FALLBACK_* settings."""
-        if self._fallback_llm is None and settings.FALLBACK_LLM_PROVIDER:
-            try:
-                from application.llm.llm_creator import LLMCreator
+        """Lazy-loaded fallback LLM: tries per-agent backup models first,
+        then the global FALLBACK_* settings."""
+        if self._fallback_llm is not None:
+            return self._fallback_llm
 
+        from application.llm.llm_creator import LLMCreator
+        from application.core.model_utils import (
+            get_provider_from_model_id,
+            get_api_key_for_provider,
+        )
+
+        # Try per-agent backup models first
+        for backup_model_id in self._backup_models:
+            try:
+                provider = get_provider_from_model_id(backup_model_id)
+                if not provider:
+                    logger.warning(
+                        f"Could not resolve provider for backup model: {backup_model_id}"
+                    )
+                    continue
+                api_key = get_api_key_for_provider(provider)
+                self._fallback_llm = LLMCreator.create_llm(
+                    provider,
+                    api_key=api_key,
+                    user_api_key=getattr(self, "user_api_key", None),
+                    decoded_token=self.decoded_token,
+                    model_id=backup_model_id,
+                    agent_id=self.agent_id,
+                )
+                logger.info(
+                    f"Fallback LLM initialized from agent backup model: "
+                    f"{provider}/{backup_model_id}"
+                )
+                return self._fallback_llm
+            except Exception as e:
+                logger.warning(
+                    f"Failed to initialize backup model {backup_model_id}: {str(e)}"
+                )
+                continue
+
+        # Fall back to global FALLBACK_* settings
+        if settings.FALLBACK_LLM_PROVIDER:
+            try:
                 self._fallback_llm = LLMCreator.create_llm(
                     settings.FALLBACK_LLM_PROVIDER,
                     api_key=settings.FALLBACK_LLM_API_KEY or settings.API_KEY,
@@ -41,12 +80,14 @@ class BaseLLM(ABC):
                     agent_id=self.agent_id,
                 )
                 logger.info(
-                    f"Fallback LLM initialized: {settings.FALLBACK_LLM_PROVIDER}/{settings.FALLBACK_LLM_NAME}"
+                    f"Fallback LLM initialized from global settings: "
+                    f"{settings.FALLBACK_LLM_PROVIDER}/{settings.FALLBACK_LLM_NAME}"
                 )
             except Exception as e:
                 logger.error(
                     f"Failed to initialize fallback LLM: {str(e)}", exc_info=True
                 )
+
         return self._fallback_llm
 
     @staticmethod
@@ -74,20 +115,60 @@ class BaseLLM(ABC):
                 method = decorator(method)
             return method(self, *args, **kwargs)
 
+        is_stream = "stream" in method_name
+
+        if is_stream:
+            return self._stream_with_fallback(
+                decorated_method, method_name, *args, **kwargs
+            )
+
         try:
             return decorated_method()
         except Exception as e:
             if not self.fallback_llm:
                 logger.error(f"Primary LLM failed and no fallback configured: {str(e)}")
                 raise
+            fallback = self.fallback_llm
             logger.warning(
-                f"Primary LLM failed. Falling back to {settings.FALLBACK_LLM_PROVIDER}/{settings.FALLBACK_LLM_NAME}. Error: {str(e)}"
+                f"Primary LLM failed. Falling back to "
+                f"{fallback.model_id}. Error: {str(e)}"
             )
 
             fallback_method = getattr(
-                self.fallback_llm, method_name.replace("_raw_", "")
+                fallback, method_name.replace("_raw_", "")
             )
-            return fallback_method(*args, **kwargs)
+            fallback_kwargs = {**kwargs, "model": fallback.model_id}
+            return fallback_method(*args, **fallback_kwargs)
+
+    def _stream_with_fallback(
+        self, decorated_method, method_name, *args, **kwargs
+    ):
+        """
+        Wrapper generator that catches mid-stream errors and falls back.
+
+        Unlike non-streaming calls where exceptions are raised immediately,
+        streaming generators raise exceptions during iteration. This wrapper
+        ensures that if the primary LLM fails at any point during streaming
+        (creation or mid-stream), we fall back to the backup model.
+        """
+        try:
+            yield from decorated_method()
+        except Exception as e:
+            if not self.fallback_llm:
+                logger.error(
+                    f"Primary LLM failed and no fallback configured: {str(e)}"
+                )
+                raise
+            fallback = self.fallback_llm
+            logger.warning(
+                f"Primary LLM failed mid-stream. Falling back to "
+                f"{fallback.model_id}. Error: {str(e)}"
+            )
+            fallback_method = getattr(
+                fallback, method_name.replace("_raw_", "")
+            )
+            fallback_kwargs = {**kwargs, "model": fallback.model_id}
+            yield from fallback_method(*args, **fallback_kwargs)
 
     def gen(self, model, messages, stream=False, tools=None, *args, **kwargs):
         decorators = [gen_token_usage, gen_cache]
diff --git a/application/llm/llm_creator.py b/application/llm/llm_creator.py
index 9f444359..d01fa2d8 100644
--- a/application/llm/llm_creator.py
+++ b/application/llm/llm_creator.py
@@ -38,6 +38,7 @@ class LLMCreator:
         decoded_token,
         model_id=None,
         agent_id=None,
+        backup_models=None,
         *args,
         **kwargs,
     ):
@@ -59,6 +60,7 @@ class LLMCreator:
             model_id=model_id,
             agent_id=agent_id,
             base_url=base_url,
+            backup_models=backup_models,
             *args,
             **kwargs,
         )
diff --git a/tests/llm/test_fallback.py b/tests/llm/test_fallback.py
new file mode 100644
index 00000000..510962ca
--- /dev/null
+++ b/tests/llm/test_fallback.py
@@ -0,0 +1,482 @@
+"""Integration tests for LLM fallback behaviour.
+
+Verifies that when a primary model fails (immediately or mid-stream), the
+per-agent backup model is used before the global FALLBACK_* settings.
+"""
+
+from unittest.mock import MagicMock
+
+import pytest
+
+from application.llm.base import BaseLLM
+
+
+# ---------------------------------------------------------------------------
+# Concrete LLM stubs
+# ---------------------------------------------------------------------------
+
+
+class FakeLLM(BaseLLM):
+    """Minimal concrete BaseLLM for testing."""
+
+    def __init__(self, responses=None, stream_chunks=None, fail_at=None, **kwargs):
+        # Accept and discard api_key / user_api_key so LLMCreator.create_llm
+        # signatures work without errors.
+        kwargs.pop("api_key", None)
+        kwargs.pop("user_api_key", None)
+        super().__init__(**kwargs)
+        self.responses = responses or ["fake response"]
+        self.stream_chunks = stream_chunks or ["chunk1", "chunk2"]
+        self.fail_at = fail_at  # None = no failure, 0 = immediate, N = after N chunks
+        self.user_api_key = None
+        self.gen_called = False
+        self.gen_stream_called = False
+        self.last_model_received = None  # tracks the model kwarg passed to gen/gen_stream
+
+    def _raw_gen(self, baseself, model, messages, stream, tools=None, **kwargs):
+        if self.fail_at is not None:
+            raise RuntimeError("primary model unavailable")
+        return self.responses[0]
+
+    def _raw_gen_stream(self, baseself, model, messages, stream, tools=None, **kwargs):
+        yielded = 0
+        for chunk in self.stream_chunks:
+            if self.fail_at is not None and yielded >= self.fail_at:
+                raise RuntimeError("mid-stream failure")
+            yield chunk
+            yielded += 1
+
+    # Wrap gen/gen_stream so we can track whether the fallback instance was used
+    # and which model kwarg it received
+    def gen(self, *args, **kwargs):
+        self.gen_called = True
+        self.last_model_received = kwargs.get("model")
+        return super().gen(*args, **kwargs)
+
+    def gen_stream(self, *args, **kwargs):
+        self.gen_stream_called = True
+        self.last_model_received = kwargs.get("model")
+        return super().gen_stream(*args, **kwargs)
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _noop_decorator(func):
+    """Pass-through decorator that replaces cache / token-usage wrappers."""
+
+    def wrapper(self_llm, model, messages, stream, tools=None, **kwargs):
+        return func(self_llm, model, messages, stream, tools, **kwargs)
+
+    return wrapper
+
+
+def _noop_stream_decorator(func):
+    """Pass-through generator decorator for streaming wrappers."""
+
+    def wrapper(self_llm, model, messages, stream, tools=None, **kwargs):
+        yield from func(self_llm, model, messages, stream, tools, **kwargs)
+
+    return wrapper
+
+
+@pytest.fixture(autouse=True)
+def _patch_decorators(monkeypatch):
+    """Replace cache & token-usage decorators with no-ops so tests focus on
+    fallback logic without needing Redis or token-counting infra."""
+    monkeypatch.setattr("application.llm.base.gen_cache", _noop_decorator)
+    monkeypatch.setattr("application.llm.base.gen_token_usage", _noop_decorator)
+    monkeypatch.setattr("application.llm.base.stream_cache", _noop_stream_decorator)
+    monkeypatch.setattr(
+        "application.llm.base.stream_token_usage", _noop_stream_decorator
+    )
+
+
+@pytest.fixture
+def patch_model_utils(monkeypatch):
+    """Patch model_utils functions used by fallback_llm property."""
+
+    def _apply(get_provider=None, get_api_key=None, create_llm=None):
+        if get_provider:
+            monkeypatch.setattr(
+                "application.core.model_utils.get_provider_from_model_id",
+                get_provider,
+            )
+        if get_api_key:
+            monkeypatch.setattr(
+                "application.core.model_utils.get_api_key_for_provider",
+                get_api_key,
+            )
+        if create_llm:
+            monkeypatch.setattr(
+                "application.llm.llm_creator.LLMCreator.create_llm",
+                create_llm,
+            )
+
+    return _apply
+
+
+CALL_ARGS = dict(model="test-model", messages=[{"role": "user", "content": "hi"}])
+
+
+# ---------------------------------------------------------------------------
+# Tests — fallback_llm property resolution
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.integration
+class TestFallbackLLMResolution:
+
+    def test_backup_model_preferred_over_global_fallback(self, patch_model_utils):
+        """When agent has backup models configured, the first valid one is used
+        as fallback — not the global FALLBACK_* settings."""
+        backup_llm = FakeLLM(responses=["backup response"])
+
+        patch_model_utils(
+            get_provider=lambda mid: "openai",
+            get_api_key=lambda prov: "fake-key",
+            create_llm=lambda type, **kw: backup_llm,
+        )
+
+        primary = FakeLLM(backup_models=["backup-model-id"])
+        fallback = primary.fallback_llm
+
+        assert fallback is backup_llm
+
+    def test_global_fallback_used_when_no_backup_models(
+        self, monkeypatch, patch_model_utils
+    ):
+        """When no per-agent backup models exist, global FALLBACK_* is used."""
+        global_fallback = FakeLLM(responses=["global fallback"])
+
+        patch_model_utils(
+            create_llm=lambda type, **kw: global_fallback,
+        )
+        monkeypatch.setattr(
+            "application.llm.base.settings",
+            MagicMock(
+                FALLBACK_LLM_PROVIDER="openai",
+                FALLBACK_LLM_NAME="gpt-4o",
+                FALLBACK_LLM_API_KEY="key",
+                API_KEY="key",
+            ),
+        )
+
+        primary = FakeLLM(backup_models=[])
+        fallback = primary.fallback_llm
+
+        assert fallback is global_fallback
+
+    def test_skips_unresolvable_backup_model_tries_next(self, patch_model_utils):
+        """If the first backup model can't be resolved, skip it and try the next."""
+        good_backup = FakeLLM(responses=["good backup"])
+        call_count = {"n": 0}
+
+        def fake_get_provider(model_id):
+            call_count["n"] += 1
+            if model_id == "bad-model":
+                return None  # unresolvable
+            return "openai"
+
+        patch_model_utils(
+            get_provider=fake_get_provider,
+            get_api_key=lambda prov: "key",
+            create_llm=lambda type, **kw: good_backup,
+        )
+
+        primary = FakeLLM(backup_models=["bad-model", "good-model"])
+        fallback = primary.fallback_llm
+
+        assert fallback is good_backup
+        assert call_count["n"] == 2  # tried both
+
+    def test_no_fallback_when_nothing_configured(self, monkeypatch):
+        """No backup models + no global FALLBACK_* → fallback_llm is None."""
+        monkeypatch.setattr(
+            "application.llm.base.settings",
+            MagicMock(FALLBACK_LLM_PROVIDER=None),
+        )
+        primary = FakeLLM(backup_models=[])
+        assert primary.fallback_llm is None
+
+
+# ---------------------------------------------------------------------------
+# Tests — non-streaming fallback (gen)
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.integration
+class TestNonStreamingFallback:
+
+    def test_primary_success_no_fallback(self):
+        """When primary succeeds, fallback is never touched."""
+        primary = FakeLLM(responses=["primary ok"])
+        result = primary.gen(**CALL_ARGS)
+        assert result == "primary ok"
+
+    def test_primary_fails_uses_backup_model(self, patch_model_utils):
+        """Primary fails immediately → backup model from agent config is used."""
+        backup = FakeLLM(responses=["backup ok"])
+
+        patch_model_utils(
+            get_provider=lambda mid: "openai",
+            get_api_key=lambda p: "k",
+            create_llm=lambda type, **kw: backup,
+        )
+
+        primary = FakeLLM(fail_at=0, backup_models=["backup-model"])
+        result = primary.gen(**CALL_ARGS)
+        assert result == "backup ok"
+        assert backup.gen_called
+
+    def test_no_fallback_raises(self, monkeypatch):
+        """Primary fails and no fallback configured → exception propagates."""
+        monkeypatch.setattr(
+            "application.llm.base.settings",
+            MagicMock(FALLBACK_LLM_PROVIDER=None),
+        )
+        primary = FakeLLM(fail_at=0, backup_models=[])
+        with pytest.raises(RuntimeError, match="primary model unavailable"):
+            primary.gen(**CALL_ARGS)
+
+
+# ---------------------------------------------------------------------------
+# Tests — streaming fallback (gen_stream)
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.integration
+class TestStreamingFallback:
+
+    def test_stream_primary_success(self):
+        """Full stream completes without triggering fallback."""
+        primary = FakeLLM(stream_chunks=["a", "b", "c"])
+        chunks = list(primary.gen_stream(**CALL_ARGS))
+        assert chunks == ["a", "b", "c"]
+
+    def test_stream_immediate_failure_uses_backup(self, patch_model_utils):
+        """Primary fails before yielding anything → entire backup stream returned."""
+        backup = FakeLLM(stream_chunks=["fallback1", "fallback2"])
+
+        patch_model_utils(
+            get_provider=lambda m: "openai",
+            get_api_key=lambda p: "k",
+            create_llm=lambda type, **kw: backup,
+        )
+
+        primary = FakeLLM(
+            stream_chunks=["x", "y"],
+            fail_at=0,  # fail before first chunk
+            backup_models=["backup-model"],
+        )
+        chunks = list(primary.gen_stream(**CALL_ARGS))
+        assert chunks == ["fallback1", "fallback2"]
+        assert backup.gen_stream_called
+
+    def test_stream_mid_stream_failure_uses_backup(self, patch_model_utils):
+        """Primary yields some chunks then fails → backup stream follows partial output."""
+        backup = FakeLLM(stream_chunks=["recovery1", "recovery2"])
+
+        patch_model_utils(
+            get_provider=lambda m: "openai",
+            get_api_key=lambda p: "k",
+            create_llm=lambda type, **kw: backup,
+        )
+
+        primary = FakeLLM(
+            stream_chunks=["ok1", "ok2", "ok3"],
+            fail_at=2,  # yields ok1, ok2, then fails before ok3
+            backup_models=["backup-model"],
+        )
+        chunks = list(primary.gen_stream(**CALL_ARGS))
+        # First two from primary, then full backup stream
+        assert chunks == ["ok1", "ok2", "recovery1", "recovery2"]
+
+    def test_stream_no_fallback_raises(self, monkeypatch):
+        """Primary stream fails and no fallback → exception propagates."""
+        monkeypatch.setattr(
+            "application.llm.base.settings",
+            MagicMock(FALLBACK_LLM_PROVIDER=None),
+        )
+        primary = FakeLLM(stream_chunks=["x"], fail_at=0, backup_models=[])
+        with pytest.raises(RuntimeError, match="mid-stream failure"):
+            list(primary.gen_stream(**CALL_ARGS))
+
+
+# ---------------------------------------------------------------------------
+# Tests — backup model priority over global fallback
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.integration
+class TestBackupModelPriority:
+
+    def test_agent_backup_tried_before_global_on_gen_failure(self, patch_model_utils):
+        """On gen() failure, agent's backup model is used — not the global fallback."""
+        backup = FakeLLM(responses=["agent backup"])
+        created_models = []
+
+        def fake_create_llm(type, **kw):
+            created_models.append(kw.get("model_id"))
+            return backup
+
+        patch_model_utils(
+            get_provider=lambda m: "openai",
+            get_api_key=lambda p: "k",
+            create_llm=fake_create_llm,
+        )
+
+        primary = FakeLLM(fail_at=0, backup_models=["agent-backup-model"])
+        result = primary.gen(**CALL_ARGS)
+
+        assert result == "agent backup"
+        assert "agent-backup-model" in created_models
+
+    def test_agent_backup_tried_before_global_on_stream_failure(
+        self, patch_model_utils
+    ):
+        """On gen_stream() failure, agent's backup model is used — not the global."""
+        backup = FakeLLM(stream_chunks=["agent-stream"])
+        created_models = []
+
+        def fake_create_llm(type, **kw):
+            created_models.append(kw.get("model_id"))
+            return backup
+
+        patch_model_utils(
+            get_provider=lambda m: "openai",
+            get_api_key=lambda p: "k",
+            create_llm=fake_create_llm,
+        )
+
+        primary = FakeLLM(
+            stream_chunks=["x"], fail_at=0, backup_models=["agent-backup-model"]
+        )
+        chunks = list(primary.gen_stream(**CALL_ARGS))
+
+        assert chunks == ["agent-stream"]
+        assert "agent-backup-model" in created_models
+
+    def test_global_fallback_used_when_all_backup_models_fail(
+        self, monkeypatch, patch_model_utils
+    ):
+        """If every agent backup model fails to initialize, fall through to global."""
+        global_fallback = FakeLLM(responses=["global ok"])
+        call_order = []
+
+        def fake_get_provider(mid):
+            if mid == "broken-backup":
+                return "nonexistent_provider"
+            return "openai"
+
+        def fake_create_llm(type, **kw):
+            model_id = kw.get("model_id")
+            call_order.append(model_id)
+            if model_id == "broken-backup":
+                raise ValueError("provider init failed")
+            return global_fallback
+
+        patch_model_utils(
+            get_provider=fake_get_provider,
+            get_api_key=lambda p: "k",
+            create_llm=fake_create_llm,
+        )
+        monkeypatch.setattr(
+            "application.llm.base.settings",
+            MagicMock(
+                FALLBACK_LLM_PROVIDER="openai",
+                FALLBACK_LLM_NAME="global-model",
+                FALLBACK_LLM_API_KEY="gk",
+                API_KEY="gk",
+            ),
+        )
+
+        primary = FakeLLM(fail_at=0, backup_models=["broken-backup"])
+        result = primary.gen(**CALL_ARGS)
+
+        assert result == "global ok"
+        # Tried broken-backup first, then fell through to global-model
+        assert call_order == ["broken-backup", "global-model"]
+
+
+# ---------------------------------------------------------------------------
+# Tests — fallback uses its own model_id, not the primary's
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.integration
+class TestFallbackModelIdOverride:
+    """The fallback LLM must be called with its own model_id — not the
+    primary's.  Otherwise providers like Groq receive an unknown model name
+    (e.g. a Qwen model_id) and return 404."""
+
+    def test_gen_fallback_receives_own_model_id(self, patch_model_utils):
+        """Non-streaming: fallback.gen() is called with fallback.model_id."""
+        backup = FakeLLM(
+            responses=["backup ok"], model_id="groq-gpt-oss-120b"
+        )
+
+        patch_model_utils(
+            get_provider=lambda m: "groq",
+            get_api_key=lambda p: "k",
+            create_llm=lambda type, **kw: backup,
+        )
+
+        primary = FakeLLM(
+            fail_at=0,
+            model_id="qwen/qwen3-4b-2507",
+            backup_models=["groq-gpt-oss-120b"],
+        )
+        result = primary.gen(**CALL_ARGS)
+
+        assert result == "backup ok"
+        assert backup.last_model_received == "groq-gpt-oss-120b"
+
+    def test_gen_stream_fallback_receives_own_model_id(self, patch_model_utils):
+        """Streaming: fallback.gen_stream() is called with fallback.model_id."""
+        backup = FakeLLM(
+            stream_chunks=["ok"], model_id="groq-gpt-oss-120b"
+        )
+
+        patch_model_utils(
+            get_provider=lambda m: "groq",
+            get_api_key=lambda p: "k",
+            create_llm=lambda type, **kw: backup,
+        )
+
+        primary = FakeLLM(
+            stream_chunks=["x"],
+            fail_at=0,
+            model_id="qwen/qwen3-4b-2507",
+            backup_models=["groq-gpt-oss-120b"],
+        )
+        chunks = list(primary.gen_stream(**CALL_ARGS))
+
+        assert chunks == ["ok"]
+        assert backup.last_model_received == "groq-gpt-oss-120b"
+
+    def test_mid_stream_fallback_receives_own_model_id(self, patch_model_utils):
+        """Mid-stream failure: fallback still gets its own model_id, not the
+        primary's that was already partially streaming."""
+        backup = FakeLLM(
+            stream_chunks=["recovered"], model_id="groq-gpt-oss-120b"
+        )
+
+        patch_model_utils(
+            get_provider=lambda m: "groq",
+            get_api_key=lambda p: "k",
+            create_llm=lambda type, **kw: backup,
+        )
+
+        primary = FakeLLM(
+            stream_chunks=["partial1", "partial2", "boom"],
+            fail_at=2,
+            model_id="qwen/qwen3-4b-2507",
+            backup_models=["groq-gpt-oss-120b"],
+        )
+        chunks = list(primary.gen_stream(**CALL_ARGS))
+
+        assert chunks == ["partial1", "partial2", "recovered"]
+        assert backup.last_model_received == "groq-gpt-oss-120b"