feat: add claude-sonnet-5 pricing to model cost map

Add Claude Sonnet 5 with introductory pricing (/0 per MTok, through
Aug 31 2026) across Anthropic, Bedrock (global + regional), Vertex AI,
Azure AI, and Snowflake providers. Includes cache pricing (1.25x write,
2x 1hr write, 0.1x read) and 128K output context.

Fixes #31868

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

litellm/constants.py

@@ -1128,6 +1128,7 @@ BEDROCK_CONVERSE_MODELS = [
     "anthropic.claude-opus-4-6-v1:0",
     "anthropic.claude-opus-4-6-v1",
     "anthropic.claude-sonnet-4-6",
+    "anthropic.claude-sonnet-5",
     "anthropic.claude-opus-4-1-20250805-v1:0",
     "anthropic.claude-opus-4-20250514-v1:0",
     "anthropic.claude-sonnet-4-20250514-v1:0",

tests/test_litellm/test_claude_sonnet_5_config.py

@@ -0,0 +1,163 @@
+"""
+Validate Claude Sonnet 5 model configuration entries.
+
+Sonnet 5 launched 2026-06-29 with introductory pricing ($2/$10 per MTok)
+through August 31, 2026, after which it moves to standard pricing ($3/$15).
+The cost-map entries use the introductory rates so spend tracking is accurate
+during the promotional period.
+"""
+
+import json
+import os
+
+import pytest
+
+import litellm
+from litellm.litellm_core_utils.get_model_cost_map import GetModelCostMap
+
+REPO_ROOT = os.path.join(os.path.dirname(__file__), "../..")
+
+
+def _load_root_cost_map() -> dict:
+    json_path = os.path.join(REPO_ROOT, "model_prices_and_context_window.json")
+    with open(json_path) as f:
+        return json.load(f)
+
+
+@pytest.fixture
+def local_model_cost_map(monkeypatch):
+    original_model_cost = litellm.model_cost
+    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
+    litellm.model_cost = litellm.get_model_cost_map(url="")
+    litellm.get_model_info.cache_clear()
+    try:
+        yield
+    finally:
+        litellm.model_cost = original_model_cost
+        litellm.get_model_info.cache_clear()
+
+
+def test_sonnet_5_model_pricing_and_capabilities():
+    model_data = _load_root_cost_map()
+
+    expected_models = [
+        ("claude-sonnet-5", "anthropic"),
+        ("anthropic.claude-sonnet-5", "bedrock_converse"),
+        ("vertex_ai/claude-sonnet-5", "vertex_ai-anthropic_models"),
+        ("azure_ai/claude-sonnet-5", "azure_ai"),
+    ]
+
+    for model_name, provider in expected_models:
+        assert model_name in model_data, f"Missing model entry: {model_name}"
+        info = model_data[model_name]
+
+        assert info["litellm_provider"] == provider
+        assert info["mode"] == "chat"
+        assert info["max_input_tokens"] == 1000000
+        assert info["max_output_tokens"] == 128000
+        assert info["max_tokens"] == 128000
+
+        assert info["input_cost_per_token"] == 2e-06
+        assert info["output_cost_per_token"] == 1e-05
+        assert info["cache_creation_input_token_cost"] == 2.5e-06
+        assert info["cache_creation_input_token_cost_above_1hr"] == 4e-06
+        assert info["cache_read_input_token_cost"] == 2e-07
+
+        assert "input_cost_per_token_above_200k_tokens" not in info
+        assert "output_cost_per_token_above_200k_tokens" not in info
+
+        assert info["supports_adaptive_thinking"] is True
+        assert info["supports_function_calling"] is True
+        assert info["supports_prompt_caching"] is True
+        assert info["supports_reasoning"] is True
+        assert info["supports_tool_choice"] is True
+        assert info["supports_vision"] is True
+
+
+def test_sonnet_5_bedrock_regional_model_pricing():
+    model_data = _load_root_cost_map()
+
+    expected_models = {
+        "global.anthropic.claude-sonnet-5": {
+            "input_cost_per_token": 2e-06,
+            "output_cost_per_token": 1e-05,
+            "cache_creation_input_token_cost": 2.5e-06,
+            "cache_read_input_token_cost": 2e-07,
+        },
+        "us.anthropic.claude-sonnet-5": {
+            "input_cost_per_token": 2.2e-06,
+            "output_cost_per_token": 1.1e-05,
+            "cache_creation_input_token_cost": 2.75e-06,
+            "cache_read_input_token_cost": 2.2e-07,
+        },
+        "eu.anthropic.claude-sonnet-5": {
+            "input_cost_per_token": 2.2e-06,
+            "output_cost_per_token": 1.1e-05,
+            "cache_creation_input_token_cost": 2.75e-06,
+            "cache_read_input_token_cost": 2.2e-07,
+        },
+        "au.anthropic.claude-sonnet-5": {
+            "input_cost_per_token": 2.2e-06,
+            "output_cost_per_token": 1.1e-05,
+            "cache_creation_input_token_cost": 2.75e-06,
+            "cache_read_input_token_cost": 2.2e-07,
+        },
+        "jp.anthropic.claude-sonnet-5": {
+            "input_cost_per_token": 2.2e-06,
+            "output_cost_per_token": 1.1e-05,
+            "cache_creation_input_token_cost": 2.75e-06,
+            "cache_read_input_token_cost": 2.2e-07,
+        },
+    }
+
+    for model_name, expected in expected_models.items():
+        assert model_name in model_data, f"Missing model entry: {model_name}"
+        info = model_data[model_name]
+        assert info["litellm_provider"] == "bedrock_converse"
+        assert info["max_input_tokens"] == 1000000
+        assert info["max_output_tokens"] == 128000
+        for key, value in expected.items():
+            assert info[key] == value, f"{model_name}.{key}: expected {value}, got {info[key]}"
+
+
+def test_sonnet_5_present_in_bundled_backup():
+    backup = GetModelCostMap.load_local_model_cost_map()
+    root = _load_root_cost_map()
+    for model_name in (
+        "claude-sonnet-5",
+        "anthropic.claude-sonnet-5",
+        "global.anthropic.claude-sonnet-5",
+        "us.anthropic.claude-sonnet-5",
+        "eu.anthropic.claude-sonnet-5",
+        "au.anthropic.claude-sonnet-5",
+        "jp.anthropic.claude-sonnet-5",
+        "vertex_ai/claude-sonnet-5",
+        "vertex_ai/claude-sonnet-5@default",
+        "azure_ai/claude-sonnet-5",
+        "snowflake/claude-sonnet-5",
+    ):
+        assert model_name in backup, f"Missing from backup cost map: {model_name}"
+        assert backup[model_name] == root[model_name], model_name
+
+
+def test_sonnet_5_provider_resolves_via_model_info(local_model_cost_map):
+    info = litellm.get_model_info(model="claude-sonnet-5")
+    assert info["litellm_provider"] == "anthropic"
+    assert info["max_input_tokens"] == 1000000
+    assert info["max_output_tokens"] == 128000
+    assert info["input_cost_per_token"] == 2e-06
+    assert info["output_cost_per_token"] == 1e-05
+
+
+@pytest.mark.parametrize(
+    "cost_map",
+    [_load_root_cost_map(), GetModelCostMap.load_local_model_cost_map()],
+    ids=["root", "bundled_backup"],
+)
+def test_sonnet_5_all_variants_carry_adaptive_thinking_flag(cost_map):
+    variants = [k for k in cost_map if "claude-sonnet-5" in k and "sonnet-4-5" not in k]
+    assert variants, "no claude-sonnet-5 entries found in cost map"
+    missing = [
+        k for k in variants if cost_map[k].get("supports_adaptive_thinking") is not True
+    ]
+    assert not missing, f"missing supports_adaptive_thinking: {missing}"